Overview

Dataset statistics

Number of variables42
Number of observations1083397
Missing cells11154479
Missing cells (%)24.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 GiB
Average record size in memory1.9 KiB

Variable types

Categorical22
Numeric17
Boolean3

Alerts

restaurant_link has a high cardinality: 1083397 distinct values High cardinality
restaurant_name has a high cardinality: 840914 distinct values High cardinality
original_location has a high cardinality: 65997 distinct values High cardinality
region has a high cardinality: 250 distinct values High cardinality
province has a high cardinality: 1333 distinct values High cardinality
city has a high cardinality: 43495 distinct values High cardinality
address has a high cardinality: 1034685 distinct values High cardinality
awards has a high cardinality: 917 distinct values High cardinality
popularity_detailed has a high cardinality: 981409 distinct values High cardinality
popularity_generic has a high cardinality: 981940 distinct values High cardinality
top_tags has a high cardinality: 39962 distinct values High cardinality
price_range has a high cardinality: 7298 distinct values High cardinality
meals has a high cardinality: 745 distinct values High cardinality
cuisines has a high cardinality: 97741 distinct values High cardinality
special_diets has a high cardinality: 68 distinct values High cardinality
features has a high cardinality: 56453 distinct values High cardinality
original_open_hours has a high cardinality: 237890 distinct values High cardinality
keywords has a high cardinality: 99001 distinct values High cardinality
open_days_per_week is highly correlated with open_hours_per_weekHigh correlation
open_hours_per_week is highly correlated with open_days_per_weekHigh correlation
avg_rating is highly correlated with food and 3 other fieldsHigh correlation
total_reviews_count is highly correlated with reviews_count_in_default_language and 3 other fieldsHigh correlation
reviews_count_in_default_language is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
excellent is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
very_good is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
average is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
poor is highly correlated with reviews_count_in_default_language and 4 other fieldsHigh correlation
terrible is highly correlated with reviews_count_in_default_language and 4 other fieldsHigh correlation
food is highly correlated with avg_rating and 3 other fieldsHigh correlation
service is highly correlated with avg_rating and 3 other fieldsHigh correlation
value is highly correlated with avg_rating and 3 other fieldsHigh correlation
atmosphere is highly correlated with avg_rating and 3 other fieldsHigh correlation
open_days_per_week is highly correlated with open_hours_per_weekHigh correlation
open_hours_per_week is highly correlated with open_days_per_weekHigh correlation
avg_rating is highly correlated with food and 3 other fieldsHigh correlation
total_reviews_count is highly correlated with reviews_count_in_default_language and 4 other fieldsHigh correlation
reviews_count_in_default_language is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
excellent is highly correlated with total_reviews_count and 4 other fieldsHigh correlation
very_good is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
average is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
poor is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
terrible is highly correlated with reviews_count_in_default_language and 3 other fieldsHigh correlation
food is highly correlated with avg_rating and 3 other fieldsHigh correlation
service is highly correlated with avg_rating and 3 other fieldsHigh correlation
value is highly correlated with avg_rating and 3 other fieldsHigh correlation
atmosphere is highly correlated with avg_rating and 3 other fieldsHigh correlation
avg_rating is highly correlated with food and 3 other fieldsHigh correlation
total_reviews_count is highly correlated with reviews_count_in_default_languageHigh correlation
reviews_count_in_default_language is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
excellent is highly correlated with reviews_count_in_default_language and 1 other fieldsHigh correlation
very_good is highly correlated with reviews_count_in_default_language and 4 other fieldsHigh correlation
average is highly correlated with reviews_count_in_default_language and 3 other fieldsHigh correlation
poor is highly correlated with reviews_count_in_default_language and 3 other fieldsHigh correlation
terrible is highly correlated with reviews_count_in_default_language and 3 other fieldsHigh correlation
food is highly correlated with avg_rating and 3 other fieldsHigh correlation
service is highly correlated with avg_rating and 3 other fieldsHigh correlation
value is highly correlated with avg_rating and 2 other fieldsHigh correlation
atmosphere is highly correlated with avg_rating and 2 other fieldsHigh correlation
gluten_free is highly correlated with vegan_options and 1 other fieldsHigh correlation
vegan_options is highly correlated with gluten_free and 2 other fieldsHigh correlation
vegetarian_friendly is highly correlated with vegan_options and 1 other fieldsHigh correlation
special_diets is highly correlated with gluten_free and 2 other fieldsHigh correlation
country is highly correlated with latitude and 1 other fieldsHigh correlation
latitude is highly correlated with country and 1 other fieldsHigh correlation
longitude is highly correlated with country and 1 other fieldsHigh correlation
claimed is highly correlated with vegetarian_friendlyHigh correlation
special_diets is highly correlated with vegetarian_friendly and 2 other fieldsHigh correlation
vegetarian_friendly is highly correlated with claimed and 4 other fieldsHigh correlation
vegan_options is highly correlated with special_diets and 2 other fieldsHigh correlation
gluten_free is highly correlated with special_diets and 2 other fieldsHigh correlation
open_days_per_week is highly correlated with open_hours_per_week and 1 other fieldsHigh correlation
open_hours_per_week is highly correlated with open_days_per_week and 1 other fieldsHigh correlation
working_shifts_per_week is highly correlated with open_days_per_week and 1 other fieldsHigh correlation
avg_rating is highly correlated with food and 3 other fieldsHigh correlation
total_reviews_count is highly correlated with reviews_count_in_default_language and 3 other fieldsHigh correlation
default_language is highly correlated with vegetarian_friendlyHigh correlation
reviews_count_in_default_language is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
excellent is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
very_good is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
average is highly correlated with total_reviews_count and 5 other fieldsHigh correlation
poor is highly correlated with reviews_count_in_default_language and 4 other fieldsHigh correlation
terrible is highly correlated with reviews_count_in_default_language and 4 other fieldsHigh correlation
food is highly correlated with avg_rating and 3 other fieldsHigh correlation
service is highly correlated with avg_rating and 3 other fieldsHigh correlation
value is highly correlated with avg_rating and 3 other fieldsHigh correlation
atmosphere is highly correlated with avg_rating and 3 other fieldsHigh correlation
region has 50323 (4.6%) missing values Missing
province has 340632 (31.4%) missing values Missing
city has 400685 (37.0%) missing values Missing
latitude has 15790 (1.5%) missing values Missing
longitude has 15790 (1.5%) missing values Missing
awards has 820264 (75.7%) missing values Missing
popularity_detailed has 94988 (8.8%) missing values Missing
popularity_generic has 97792 (9.0%) missing values Missing
top_tags has 110634 (10.2%) missing values Missing
price_level has 277205 (25.6%) missing values Missing
price_range has 779070 (71.9%) missing values Missing
meals has 448050 (41.4%) missing values Missing
cuisines has 169103 (15.6%) missing values Missing
special_diets has 743141 (68.6%) missing values Missing
features has 765990 (70.7%) missing values Missing
original_open_hours has 489565 (45.2%) missing values Missing
open_days_per_week has 489565 (45.2%) missing values Missing
open_hours_per_week has 489565 (45.2%) missing values Missing
working_shifts_per_week has 489565 (45.2%) missing values Missing
avg_rating has 96636 (8.9%) missing values Missing
total_reviews_count has 52235 (4.8%) missing values Missing
default_language has 95193 (8.8%) missing values Missing
reviews_count_in_default_language has 95193 (8.8%) missing values Missing
excellent has 95193 (8.8%) missing values Missing
very_good has 95193 (8.8%) missing values Missing
average has 95193 (8.8%) missing values Missing
poor has 95193 (8.8%) missing values Missing
terrible has 95193 (8.8%) missing values Missing
food has 484072 (44.7%) missing values Missing
service has 479110 (44.2%) missing values Missing
value has 480705 (44.4%) missing values Missing
atmosphere has 821612 (75.8%) missing values Missing
keywords has 984199 (90.8%) missing values Missing
total_reviews_count is highly skewed (γ1 = 25.28240244) Skewed
average is highly skewed (γ1 = 21.42675175) Skewed
restaurant_link is uniformly distributed Uniform
address is uniformly distributed Uniform
popularity_detailed is uniformly distributed Uniform
popularity_generic is uniformly distributed Uniform
keywords is uniformly distributed Uniform
restaurant_link has unique values Unique
total_reviews_count has 44149 (4.1%) zeros Zeros
excellent has 146592 (13.5%) zeros Zeros
very_good has 278879 (25.7%) zeros Zeros
average has 493840 (45.6%) zeros Zeros
poor has 614652 (56.7%) zeros Zeros
terrible has 573943 (53.0%) zeros Zeros

Reproduction

Analysis started2021-12-08 05:53:21.849890
Analysis finished2021-12-08 06:05:07.011385
Duration11 minutes and 45.16 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

restaurant_link
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1083397
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size76.2 MiB
g4173422-d19235050
 
1
g7377416-d2002991
 
1
g1073586-d14939977
 
1
g187791-d15780017
 
1
g187803-d12133958
 
1
Other values (1083392)
1083392 

Length

Max length19
Median length17
Mean length16.73728282
Min length15

Characters and Unicode

Total characters18133122
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1083397 ?
Unique (%)100.0%

Sample

1st rowg10001637-d10002227
2nd rowg10001637-d14975787
3rd rowg10002858-d4586832
4th rowg10002986-d3510044
5th rowg10022428-d9767191

Common Values

ValueCountFrequency (%)
g4173422-d192350501
 
< 0.1%
g7377416-d20029911
 
< 0.1%
g1073586-d149399771
 
< 0.1%
g187791-d157800171
 
< 0.1%
g187803-d121339581
 
< 0.1%
g187458-d213604731
 
< 0.1%
g1547037-d49193421
 
< 0.1%
g504128-d101466001
 
< 0.1%
g3320396-d36274251
 
< 0.1%
g804273-d190829121
 
< 0.1%
Other values (1083387)1083387
> 99.9%

Length

2021-12-08T01:05:07.659923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g4173422-d192350501
 
< 0.1%
g187391-d32413041
 
< 0.1%
g316018-d19783811
 
< 0.1%
g8429732-d83426111
 
< 0.1%
g503723-d209413651
 
< 0.1%
g274914-d22890551
 
< 0.1%
g186338-d157040101
 
< 0.1%
g503808-d210681751
 
< 0.1%
g2429250-d119200171
 
< 0.1%
g9595282-d124469771
 
< 0.1%
Other values (1083387)1083387
> 99.9%

Most occurring characters

ValueCountFrequency (%)
12367706
13.1%
81676353
9.2%
71560090
8.6%
21446741
8.0%
41346138
7.4%
61335721
7.4%
31318883
7.3%
51281134
 
7.1%
01276116
 
7.0%
91274049
 
7.0%
Other values (3)3250191
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14882931
82.1%
Lowercase Letter2166794
 
11.9%
Dash Punctuation1083397
 
6.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12367706
15.9%
81676353
11.3%
71560090
10.5%
21446741
9.7%
41346138
9.0%
61335721
9.0%
31318883
8.9%
51281134
8.6%
01276116
8.6%
91274049
8.6%
Lowercase Letter
ValueCountFrequency (%)
g1083397
50.0%
d1083397
50.0%
Dash Punctuation
ValueCountFrequency (%)
-1083397
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common15966328
88.1%
Latin2166794
 
11.9%

Most frequent character per script

Common
ValueCountFrequency (%)
12367706
14.8%
81676353
10.5%
71560090
9.8%
21446741
9.1%
41346138
8.4%
61335721
8.4%
31318883
8.3%
51281134
8.0%
01276116
8.0%
91274049
8.0%
Latin
ValueCountFrequency (%)
g1083397
50.0%
d1083397
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18133122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12367706
13.1%
81676353
9.2%
71560090
8.6%
21446741
8.0%
41346138
7.4%
61335721
7.4%
31318883
7.3%
51281134
 
7.1%
01276116
 
7.0%
91274049
 
7.0%
Other values (3)3250191
17.9%

restaurant_name
Categorical

HIGH CARDINALITY

Distinct840914
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Memory size77.3 MiB
Subway
 
4881
McDonald's
 
4458
Burger King
 
2480
Domino's Pizza
 
2163
KFC
 
1501
Other values (840909)
1067914 

Length

Max length105
Median length15
Mean length16.36735933
Min length1

Characters and Unicode

Total characters17732348
Distinct characters525
Distinct categories24 ?
Distinct scripts10 ?
Distinct blocks20 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique768249 ?
Unique (%)70.9%

Sample

1st rowLe 147
2nd rowLe Saint Jouvent
3rd rowAu Bout du Pont
4th rowLe Relais de Naiade
5th rowRelais Du MontSeigne

Common Values

ValueCountFrequency (%)
Subway4881
 
0.5%
McDonald's4458
 
0.4%
Burger King2480
 
0.2%
Domino's Pizza2163
 
0.2%
KFC1501
 
0.1%
Costa Coffee1367
 
0.1%
Starbucks1119
 
0.1%
Pizza Hut992
 
0.1%
Wild Bean Cafe920
 
0.1%
BP629
 
0.1%
Other values (840904)1062887
98.1%

Length

2021-12-08T01:05:09.366603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
la80549
 
2.9%
restaurant68041
 
2.4%
63134
 
2.2%
bar59221
 
2.1%
the46634
 
1.7%
cafe45343
 
1.6%
le38849
 
1.4%
pizzeria35085
 
1.2%
de33968
 
1.2%
ristorante29268
 
1.0%
Other values (342372)2322299
82.3%

Most occurring characters

ValueCountFrequency (%)
a1949331
 
11.0%
1741579
 
9.8%
e1614156
 
9.1%
r1122456
 
6.3%
i1111677
 
6.3%
o971826
 
5.5%
t900478
 
5.1%
n874322
 
4.9%
s784395
 
4.4%
l641495
 
3.6%
Other values (515)6020633
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13003878
73.3%
Uppercase Letter2723843
 
15.4%
Space Separator1741693
 
9.8%
Other Punctuation156647
 
0.9%
Decimal Number57223
 
0.3%
Dash Punctuation39849
 
0.2%
Final Punctuation3552
 
< 0.1%
Open Punctuation1418
 
< 0.1%
Close Punctuation1382
 
< 0.1%
Modifier Symbol850
 
< 0.1%
Other values (14)2013
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1949331
15.0%
e1614156
12.4%
r1122456
8.6%
i1111677
8.5%
o971826
 
7.5%
t900478
 
6.9%
n874322
 
6.7%
s784395
 
6.0%
l641495
 
4.9%
u508203
 
3.9%
Other values (166)2525539
19.4%
Uppercase Letter
ValueCountFrequency (%)
C282760
 
10.4%
B238570
 
8.8%
P226554
 
8.3%
R218269
 
8.0%
L215980
 
7.9%
S179794
 
6.6%
T172625
 
6.3%
A146714
 
5.4%
M145033
 
5.3%
D115710
 
4.2%
Other values (126)781834
28.7%
Other Letter
ValueCountFrequency (%)
º35
 
18.4%
ª14
 
7.4%
ا7
 
3.7%
ل5
 
2.6%
5
 
2.6%
ن4
 
2.1%
3
 
1.6%
3
 
1.6%
ه3
 
1.6%
ب3
 
1.6%
Other values (89)108
56.8%
Other Punctuation
ValueCountFrequency (%)
'85471
54.6%
&44219
28.2%
.14769
 
9.4%
,4632
 
3.0%
"3626
 
2.3%
!1282
 
0.8%
/1126
 
0.7%
@467
 
0.3%
:287
 
0.2%
#260
 
0.2%
Other values (12)508
 
0.3%
Decimal Number
ValueCountFrequency (%)
111168
19.5%
29127
15.9%
07520
13.1%
35656
9.9%
44370
 
7.6%
94361
 
7.6%
54122
 
7.2%
83853
 
6.7%
73524
 
6.2%
63522
 
6.2%
Nonspacing Mark
ValueCountFrequency (%)
́141
42.0%
̈133
39.6%
̀41
 
12.2%
̌7
 
2.1%
̃6
 
1.8%
̂4
 
1.2%
ِ1
 
0.3%
1
 
0.3%
̊1
 
0.3%
̧1
 
0.3%
Other Symbol
ValueCountFrequency (%)
°105
67.3%
®41
 
26.3%
2
 
1.3%
©2
 
1.3%
¦1
 
0.6%
🌏1
 
0.6%
🌍1
 
0.6%
🌎1
 
0.6%
🍅1
 
0.6%
1
 
0.6%
Math Symbol
ValueCountFrequency (%)
+582
76.0%
|156
 
20.4%
~14
 
1.8%
=6
 
0.8%
3
 
0.4%
>3
 
0.4%
1
 
0.1%
1
 
0.1%
Space Separator
ValueCountFrequency (%)
1741579
> 99.9%
 110
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
 1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
´537
63.2%
`301
35.4%
¨7
 
0.8%
˝2
 
0.2%
^2
 
0.2%
΄1
 
0.1%
Format
ValueCountFrequency (%)
22
41.5%
18
34.0%
­7
 
13.2%
3
 
5.7%
2
 
3.8%
1
 
1.9%
Open Punctuation
ValueCountFrequency (%)
(1357
95.7%
32
 
2.3%
[27
 
1.9%
{1
 
0.1%
1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-39692
99.6%
153
 
0.4%
3
 
< 0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
)1354
98.0%
]26
 
1.9%
}1
 
0.1%
1
 
0.1%
Modifier Letter
ValueCountFrequency (%)
ˋ2
40.0%
1
20.0%
1
20.0%
ʾ1
20.0%
Final Punctuation
ValueCountFrequency (%)
3469
97.7%
69
 
1.9%
»14
 
0.4%
Initial Punctuation
ValueCountFrequency (%)
207
65.3%
96
30.3%
«14
 
4.4%
Other Number
ValueCountFrequency (%)
²20
87.0%
¾2
 
8.7%
³1
 
4.3%
Currency Symbol
ValueCountFrequency (%)
5
45.5%
$4
36.4%
£2
 
18.2%
Control
ValueCountFrequency (%)
’1
33.3%
1
33.3%
„1
33.3%
Connector Punctuation
ValueCountFrequency (%)
_149
100.0%
Spacing Mark
ValueCountFrequency (%)
2
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Line Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15724854
88.7%
Common2004095
 
11.3%
Greek2570
 
< 0.1%
Cyrillic350
 
< 0.1%
Inherited335
 
< 0.1%
Han83
 
< 0.1%
Arabic42
 
< 0.1%
Hebrew7
 
< 0.1%
Devanagari6
 
< 0.1%
Katakana6
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1949331
 
12.4%
e1614156
 
10.3%
r1122456
 
7.1%
i1111677
 
7.1%
o971826
 
6.2%
t900478
 
5.7%
n874322
 
5.6%
s784395
 
5.0%
l641495
 
4.1%
u508203
 
3.2%
Other values (199)5246515
33.4%
Common
ValueCountFrequency (%)
1741579
86.9%
'85471
 
4.3%
&44219
 
2.2%
-39692
 
2.0%
.14769
 
0.7%
111168
 
0.6%
29127
 
0.5%
07520
 
0.4%
35656
 
0.3%
,4632
 
0.2%
Other values (89)40262
 
2.0%
Han
ValueCountFrequency (%)
5
 
6.0%
3
 
3.6%
3
 
3.6%
3
 
3.6%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (53)57
68.7%
Greek
ValueCountFrequency (%)
α295
 
11.5%
ο232
 
9.0%
ι148
 
5.8%
ν131
 
5.1%
ρ128
 
5.0%
τ113
 
4.4%
ε98
 
3.8%
Τ93
 
3.6%
κ86
 
3.3%
λ84
 
3.3%
Other values (50)1162
45.2%
Cyrillic
ValueCountFrequency (%)
а57
16.3%
н29
 
8.3%
т28
 
8.0%
р26
 
7.4%
е24
 
6.9%
и21
 
6.0%
о19
 
5.4%
с15
 
4.3%
к12
 
3.4%
л10
 
2.9%
Other values (39)109
31.1%
Arabic
ValueCountFrequency (%)
ا7
16.7%
ل5
11.9%
ن4
9.5%
ه3
 
7.1%
ب3
 
7.1%
ي3
 
7.1%
م2
 
4.8%
د2
 
4.8%
ر2
 
4.8%
ک2
 
4.8%
Other values (9)9
21.4%
Inherited
ValueCountFrequency (%)
́141
42.1%
̈133
39.7%
̀41
 
12.2%
̌7
 
2.1%
̃6
 
1.8%
̂4
 
1.2%
ِ1
 
0.3%
̊1
 
0.3%
̧1
 
0.3%
Hebrew
ValueCountFrequency (%)
ק2
28.6%
ם1
14.3%
ר1
14.3%
ט1
14.3%
ש1
14.3%
כ1
14.3%
Katakana
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Devanagari
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII17659195
99.6%
None68133
 
0.4%
Punctuation4150
 
< 0.1%
Cyrillic350
 
< 0.1%
Diacriticals334
 
< 0.1%
CJK83
 
< 0.1%
Arabic43
 
< 0.1%
Latin Ext Additional20
 
< 0.1%
Hebrew7
 
< 0.1%
Devanagari6
 
< 0.1%
Other values (10)27
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1949331
 
11.0%
1741579
 
9.9%
e1614156
 
9.1%
r1122456
 
6.4%
i1111677
 
6.3%
o971826
 
5.5%
t900478
 
5.1%
n874322
 
5.0%
s784395
 
4.4%
l641495
 
3.6%
Other values (85)5947480
33.7%
None
ValueCountFrequency (%)
é17796
26.1%
í5674
 
8.3%
è5598
 
8.2%
ó3521
 
5.2%
á3486
 
5.1%
ü3171
 
4.7%
ä3157
 
4.6%
ö2963
 
4.3%
à1926
 
2.8%
ñ1416
 
2.1%
Other values (222)19425
28.5%
Punctuation
ValueCountFrequency (%)
3469
83.6%
207
 
5.0%
153
 
3.7%
96
 
2.3%
71
 
1.7%
69
 
1.7%
32
 
0.8%
22
 
0.5%
18
 
0.4%
3
 
0.1%
Other values (8)10
 
0.2%
Diacriticals
ValueCountFrequency (%)
́141
42.2%
̈133
39.8%
̀41
 
12.3%
̌7
 
2.1%
̃6
 
1.8%
̂4
 
1.2%
̊1
 
0.3%
̧1
 
0.3%
Cyrillic
ValueCountFrequency (%)
а57
16.3%
н29
 
8.3%
т28
 
8.0%
р26
 
7.4%
е24
 
6.9%
и21
 
6.0%
о19
 
5.4%
с15
 
4.3%
к12
 
3.4%
л10
 
2.9%
Other values (39)109
31.1%
Arabic
ValueCountFrequency (%)
ا7
16.3%
ل5
11.6%
ن4
 
9.3%
ه3
 
7.0%
ب3
 
7.0%
ي3
 
7.0%
م2
 
4.7%
د2
 
4.7%
ر2
 
4.7%
ک2
 
4.7%
Other values (10)10
23.3%
Latin Ext Additional
ValueCountFrequency (%)
5
25.0%
5
25.0%
2
 
10.0%
1
 
5.0%
ế1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Currency Symbols
ValueCountFrequency (%)
5
100.0%
CJK
ValueCountFrequency (%)
5
 
6.0%
3
 
3.6%
3
 
3.6%
3
 
3.6%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
2
 
2.4%
Other values (53)57
68.7%
Math Operators
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
Letterlike Symbols
ValueCountFrequency (%)
2
100.0%
Modifier Letters
ValueCountFrequency (%)
˝2
40.0%
ˋ2
40.0%
ʾ1
20.0%
Hebrew
ValueCountFrequency (%)
ק2
28.6%
ם1
14.3%
ר1
14.3%
ט1
14.3%
ש1
14.3%
כ1
14.3%
Devanagari
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Number Forms
ValueCountFrequency (%)
1
100.0%
Katakana
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Phonetic Ext
ValueCountFrequency (%)
1
100.0%
Misc Technical
ValueCountFrequency (%)
1
100.0%
Phonetic Ext Sup
ValueCountFrequency (%)
1
100.0%
Misc Symbols
ValueCountFrequency (%)
1
100.0%

original_location
Categorical

HIGH CARDINALITY

Distinct65997
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size127.9 MiB
["Europe", "United Kingdom (UK)", "England", "London"]
 
22942
["Europe", "France", "Ile-de-France", "Paris"]
 
18129
["Europe", "Italy", "Lazio", "Rome"]
 
12603
["Europe", "Spain", "Community of Madrid", "Madrid"]
 
12134
["Europe", "Spain", "Catalonia", "Province of Barcelona", "Barcelona"]
 
10285
Other values (65992)
1007304 

Length

Max length151
Median length67
Mean length66.78260601
Min length19

Characters and Unicode

Total characters72352075
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21413 ?
Unique (%)2.0%

Sample

1st row["Europe", "France", "Nouvelle-Aquitaine", "Haute-Vienne", "Saint-Jouvent"]
2nd row["Europe", "France", "Nouvelle-Aquitaine", "Haute-Vienne", "Saint-Jouvent"]
3rd row["Europe", "France", "Centre-Val de Loire", "Berry", "Indre", "Rivarennes"]
4th row["Europe", "France", "Nouvelle-Aquitaine", "Correze", "Lacelle"]
5th row["Europe", "France", "Occitanie", "Aveyron", "Saint-Laurent-de-Levezou"]

Common Values

ValueCountFrequency (%)
["Europe", "United Kingdom (UK)", "England", "London"]22942
 
2.1%
["Europe", "France", "Ile-de-France", "Paris"]18129
 
1.7%
["Europe", "Italy", "Lazio", "Rome"]12603
 
1.2%
["Europe", "Spain", "Community of Madrid", "Madrid"]12134
 
1.1%
["Europe", "Spain", "Catalonia", "Province of Barcelona", "Barcelona"]10285
 
0.9%
["Europe", "Italy", "Lombardy", "Milan"]8382
 
0.8%
["Europe", "Germany", "Berlin"]7217
 
0.7%
["Europe", "Czech Republic", "Bohemia", "Prague"]6035
 
0.6%
["Europe", "Portugal", "Central Portugal", "Lisbon District", "Lisbon"]5261
 
0.5%
["Europe", "Austria", "Vienna Region", "Vienna"]4571
 
0.4%
Other values (65987)975838
90.1%

Length

2021-12-08T01:05:09.699889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
europe1083397
 
14.7%
province381688
 
5.2%
of336316
 
4.6%
italy224763
 
3.0%
kingdom171664
 
2.3%
uk171664
 
2.3%
united171664
 
2.3%
spain157486
 
2.1%
france155288
 
2.1%
england144681
 
2.0%
Other values (61028)4384880
59.4%

Most occurring characters

ValueCountFrequency (%)
"10516036
14.5%
6300149
 
8.7%
e5091386
 
7.0%
a4329028
 
6.0%
,4174631
 
5.8%
o4039912
 
5.6%
r4013033
 
5.5%
n3815234
 
5.3%
i2982907
 
4.1%
u2053406
 
2.8%
Other values (61)25036353
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40851093
56.5%
Other Punctuation14739954
 
20.4%
Uppercase Letter7462877
 
10.3%
Space Separator6300149
 
8.7%
Close Punctuation1256228
 
1.7%
Open Punctuation1256228
 
1.7%
Dash Punctuation484731
 
0.7%
Decimal Number815
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5091386
12.5%
a4329028
10.6%
o4039912
9.9%
r4013033
9.8%
n3815234
9.3%
i2982907
 
7.3%
u2053406
 
5.0%
l2019889
 
4.9%
t1928588
 
4.7%
p1500452
 
3.7%
Other values (16)9077258
22.2%
Uppercase Letter
ValueCountFrequency (%)
E1317168
17.6%
P737802
 
9.9%
S527168
 
7.1%
C515208
 
6.9%
K388069
 
5.2%
A384463
 
5.2%
U378131
 
5.1%
B348781
 
4.7%
I347383
 
4.7%
F320957
 
4.3%
Other values (16)2197747
29.4%
Decimal Number
ValueCountFrequency (%)
0307
37.7%
2221
27.1%
1144
17.7%
399
 
12.1%
942
 
5.2%
71
 
0.1%
41
 
0.1%
Other Punctuation
ValueCountFrequency (%)
"10516036
71.3%
,4174631
 
28.3%
'48258
 
0.3%
.606
 
< 0.1%
\422
 
< 0.1%
&1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
]1083397
86.2%
)172831
 
13.8%
Open Punctuation
ValueCountFrequency (%)
[1083397
86.2%
(172831
 
13.8%
Space Separator
ValueCountFrequency (%)
6300149
100.0%
Dash Punctuation
ValueCountFrequency (%)
-484731
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin48313970
66.8%
Common24038105
33.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5091386
 
10.5%
a4329028
 
9.0%
o4039912
 
8.4%
r4013033
 
8.3%
n3815234
 
7.9%
i2982907
 
6.2%
u2053406
 
4.3%
l2019889
 
4.2%
t1928588
 
4.0%
p1500452
 
3.1%
Other values (42)16540135
34.2%
Common
ValueCountFrequency (%)
"10516036
43.7%
6300149
26.2%
,4174631
 
17.4%
]1083397
 
4.5%
[1083397
 
4.5%
-484731
 
2.0%
)172831
 
0.7%
(172831
 
0.7%
'48258
 
0.2%
.606
 
< 0.1%
Other values (9)1238
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII72352075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
"10516036
14.5%
6300149
 
8.7%
e5091386
 
7.0%
a4329028
 
6.0%
,4174631
 
5.8%
o4039912
 
5.6%
r4013033
 
5.5%
n3815234
 
5.3%
i2982907
 
4.1%
u2053406
 
2.8%
Other values (61)25036353
34.6%

country
Categorical

HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.6 MiB
Italy
224763 
Spain
157479 
France
155288 
England
144681 
Germany
115333 
Other values (19)
285853 

Length

Max length16
Median length6
Mean length6.460420326
Min length5

Characters and Unicode

Total characters6999200
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFrance
2nd rowFrance
3rd rowFrance
4th rowFrance
5th rowFrance

Common Values

ValueCountFrequency (%)
Italy224763
20.7%
Spain157479
14.5%
France155288
14.3%
England144681
13.4%
Germany115333
10.6%
Greece33763
 
3.1%
Portugal32592
 
3.0%
The Netherlands29792
 
2.7%
Poland24698
 
2.3%
Belgium23711
 
2.2%
Other values (14)141297
13.0%

Length

2021-12-08T01:05:09.924894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
italy224763
19.9%
spain157479
13.9%
france155288
13.7%
england144681
12.8%
germany115333
10.2%
greece33763
 
3.0%
portugal32592
 
2.9%
the29792
 
2.6%
netherlands29792
 
2.6%
poland24698
 
2.2%
Other values (16)183486
16.2%

Most occurring characters

ValueCountFrequency (%)
a1017461
14.5%
n862695
12.3%
e588885
 
8.4%
l549359
 
7.8%
r439120
 
6.3%
y347527
 
5.0%
t333858
 
4.8%
d254150
 
3.6%
i248830
 
3.6%
I239600
 
3.4%
Other values (28)2117715
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5819263
83.1%
Uppercase Letter1131667
 
16.2%
Space Separator48270
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1017461
17.5%
n862695
14.8%
e588885
10.1%
l549359
9.4%
r439120
7.5%
y347527
 
6.0%
t333858
 
5.7%
d254150
 
4.4%
i248830
 
4.3%
c232954
 
4.0%
Other values (12)944424
16.2%
Uppercase Letter
ValueCountFrequency (%)
I239600
21.2%
S194500
17.2%
F162660
14.4%
G149096
13.2%
E144681
12.8%
P57290
 
5.1%
N33426
 
3.0%
T29792
 
2.6%
B28180
 
2.5%
C23219
 
2.1%
Other values (5)69223
 
6.1%
Space Separator
ValueCountFrequency (%)
48270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6950930
99.3%
Common48270
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1017461
14.6%
n862695
12.4%
e588885
 
8.5%
l549359
 
7.9%
r439120
 
6.3%
y347527
 
5.0%
t333858
 
4.8%
d254150
 
3.7%
i248830
 
3.6%
I239600
 
3.4%
Other values (27)2069445
29.8%
Common
ValueCountFrequency (%)
48270
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII6999200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1017461
14.5%
n862695
12.3%
e588885
 
8.4%
l549359
 
7.8%
r439120
 
6.3%
y347527
 
5.0%
t333858
 
4.8%
d254150
 
3.6%
i248830
 
3.6%
I239600
 
3.4%
Other values (28)2117715
30.3%

region
Categorical

HIGH CARDINALITY
MISSING

Distinct250
Distinct (%)< 0.1%
Missing50323
Missing (%)4.6%
Memory size69.1 MiB
Lombardy
 
33097
Ile-de-France
 
31271
Andalucia
 
29562
Catalonia
 
28569
Lazio
 
23831
Other values (245)
886744 

Length

Max length28
Median length9
Mean length11.56804837
Min length4

Characters and Unicode

Total characters11950650
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowNouvelle-Aquitaine
2nd rowNouvelle-Aquitaine
3rd rowCentre-Val de Loire
4th rowNouvelle-Aquitaine
5th rowOccitanie

Common Values

ValueCountFrequency (%)
Lombardy33097
 
3.1%
Ile-de-France31271
 
2.9%
Andalucia29562
 
2.7%
Catalonia28569
 
2.6%
Lazio23831
 
2.2%
London22942
 
2.1%
Bavaria21531
 
2.0%
North Rhine-Westphalia21116
 
1.9%
Auvergne-Rhone-Alpes20753
 
1.9%
Provence-Alpes-Cote d'Azur19925
 
1.8%
Other values (240)780477
72.0%
(Missing)50323
 
4.6%

Length

2021-12-08T01:05:10.103509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of33839
 
2.4%
lombardy33097
 
2.3%
central32835
 
2.3%
ile-de-france31271
 
2.2%
andalucia29562
 
2.1%
london29388
 
2.0%
catalonia28569
 
2.0%
islands28244
 
2.0%
poland24698
 
1.7%
north24377
 
1.7%
Other values (262)1138158
79.4%

Most occurring characters

ValueCountFrequency (%)
a1296788
 
10.9%
e1104772
 
9.2%
n935839
 
7.8%
r806044
 
6.7%
i784125
 
6.6%
o698710
 
5.8%
t593900
 
5.0%
l567339
 
4.7%
s461365
 
3.9%
d408191
 
3.4%
Other values (43)4293577
35.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9656415
80.8%
Uppercase Letter1601456
 
13.4%
Space Separator400964
 
3.4%
Dash Punctuation270679
 
2.3%
Other Punctuation21136
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1296788
13.4%
e1104772
11.4%
n935839
9.7%
r806044
8.3%
i784125
 
8.1%
o698710
 
7.2%
t593900
 
6.2%
l567339
 
5.9%
s461365
 
4.8%
d408191
 
4.2%
Other values (16)1999342
20.7%
Uppercase Letter
ValueCountFrequency (%)
C207371
12.9%
A205346
12.8%
L145737
 
9.1%
P128733
 
8.0%
B106354
 
6.6%
S90956
 
5.7%
R83882
 
5.2%
W76469
 
4.8%
N70134
 
4.4%
M65597
 
4.1%
Other values (14)420877
26.3%
Space Separator
ValueCountFrequency (%)
400964
100.0%
Dash Punctuation
ValueCountFrequency (%)
-270679
100.0%
Other Punctuation
ValueCountFrequency (%)
'21136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11257871
94.2%
Common692779
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1296788
 
11.5%
e1104772
 
9.8%
n935839
 
8.3%
r806044
 
7.2%
i784125
 
7.0%
o698710
 
6.2%
t593900
 
5.3%
l567339
 
5.0%
s461365
 
4.1%
d408191
 
3.6%
Other values (40)3600798
32.0%
Common
ValueCountFrequency (%)
400964
57.9%
-270679
39.1%
'21136
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11950650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1296788
 
10.9%
e1104772
 
9.2%
n935839
 
7.8%
r806044
 
6.7%
i784125
 
6.6%
o698710
 
5.8%
t593900
 
5.0%
l567339
 
4.7%
s461365
 
3.9%
d408191
 
3.4%
Other values (43)4293577
35.9%

province
Categorical

HIGH CARDINALITY
MISSING

Distinct1333
Distinct (%)0.2%
Missing340632
Missing (%)31.4%
Memory size62.0 MiB
Province of Barcelona
 
18952
Province of Malaga
 
10056
Province of Alicante
 
9137
Province of Naples
 
8962
Upper Bavaria
 
8584
Other values (1328)
687074 

Length

Max length43
Median length17
Mean length15.90769759
Min length3

Characters and Unicode

Total characters11815681
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)< 0.1%

Sample

1st rowHaute-Vienne
2nd rowHaute-Vienne
3rd rowBerry
4th rowCorreze
5th rowAveyron

Common Values

ValueCountFrequency (%)
Province of Barcelona18952
 
1.7%
Province of Malaga10056
 
0.9%
Province of Alicante9137
 
0.8%
Province of Naples8962
 
0.8%
Upper Bavaria8584
 
0.8%
Lisbon District8223
 
0.8%
French Riviera - Cote d'Azur8156
 
0.8%
Province of Turin7846
 
0.7%
North Holland Province7620
 
0.7%
Province of Valencia7337
 
0.7%
Other values (1323)647892
59.8%
(Missing)340632
31.4%

Length

2021-12-08T01:05:10.308150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
province371190
21.7%
of299513
 
17.5%
county49203
 
2.9%
district33745
 
2.0%
region24735
 
1.4%
barcelona18952
 
1.1%
north16813
 
1.0%
south16341
 
1.0%
riviera15840
 
0.9%
yorkshire13672
 
0.8%
Other values (1463)848979
49.7%

Most occurring characters

ValueCountFrequency (%)
o1219746
 
10.3%
e1089141
 
9.2%
966218
 
8.2%
r938288
 
7.9%
i916432
 
7.8%
n909976
 
7.7%
a830518
 
7.0%
c550953
 
4.7%
v453260
 
3.8%
P435671
 
3.7%
Other values (47)3505478
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9254671
78.3%
Uppercase Letter1466596
 
12.4%
Space Separator966218
 
8.2%
Dash Punctuation114832
 
1.0%
Other Punctuation13360
 
0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o1219746
13.2%
e1089141
11.8%
r938288
10.1%
i916432
9.9%
n909976
9.8%
a830518
9.0%
c550953
 
6.0%
v453260
 
4.9%
t392009
 
4.2%
f321533
 
3.5%
Other values (16)1632815
17.6%
Uppercase Letter
ValueCountFrequency (%)
P435671
29.7%
C130457
 
8.9%
S88680
 
6.0%
B83698
 
5.7%
R75606
 
5.2%
L71353
 
4.9%
M64552
 
4.4%
A62645
 
4.3%
D54253
 
3.7%
V51440
 
3.5%
Other values (16)348241
23.7%
Space Separator
ValueCountFrequency (%)
966218
100.0%
Dash Punctuation
ValueCountFrequency (%)
-114832
100.0%
Other Punctuation
ValueCountFrequency (%)
'13360
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10721267
90.7%
Common1094414
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o1219746
11.4%
e1089141
 
10.2%
r938288
 
8.8%
i916432
 
8.5%
n909976
 
8.5%
a830518
 
7.7%
c550953
 
5.1%
v453260
 
4.2%
P435671
 
4.1%
t392009
 
3.7%
Other values (42)2985273
27.8%
Common
ValueCountFrequency (%)
966218
88.3%
-114832
 
10.5%
'13360
 
1.2%
(2
 
< 0.1%
)2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11815681
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o1219746
 
10.3%
e1089141
 
9.2%
966218
 
8.2%
r938288
 
7.9%
i916432
 
7.8%
n909976
 
7.7%
a830518
 
7.0%
c550953
 
4.7%
v453260
 
3.8%
P435671
 
3.7%
Other values (47)3505478
29.7%

city
Categorical

HIGH CARDINALITY
MISSING

Distinct43495
Distinct (%)6.4%
Missing400685
Missing (%)37.0%
Memory size55.2 MiB
Paris
 
18129
Rome
 
12603
Madrid
 
12134
Milan
 
8382
Prague
 
6035
Other values (43490)
625429 

Length

Max length48
Median length8
Mean length9.03642971
Min length2

Characters and Unicode

Total characters6169279
Distinct characters66
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14579 ?
Unique (%)2.1%

Sample

1st rowSaint-Jouvent
2nd rowSaint-Jouvent
3rd rowRivarennes
4th rowLacelle
5th rowSaint-Laurent-de-Levezou

Common Values

ValueCountFrequency (%)
Paris18129
 
1.7%
Rome12603
 
1.2%
Madrid12134
 
1.1%
Milan8382
 
0.8%
Prague6035
 
0.6%
Lisbon5261
 
0.5%
Vienna4571
 
0.4%
Amsterdam4352
 
0.4%
Budapest3557
 
0.3%
Munich3508
 
0.3%
Other values (43485)604180
55.8%
(Missing)400685
37.0%

Length

2021-12-08T01:05:10.604698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
paris18129
 
2.2%
de15706
 
1.9%
rome12603
 
1.5%
madrid12156
 
1.5%
milan8382
 
1.0%
la7449
 
0.9%
prague6035
 
0.7%
lisbon5266
 
0.6%
vienna4571
 
0.6%
amsterdam4352
 
0.5%
Other values (42902)733574
88.6%

Most occurring characters

ValueCountFrequency (%)
e648027
 
10.5%
a564130
 
9.1%
r447685
 
7.3%
n438152
 
7.1%
o387285
 
6.3%
i375702
 
6.1%
l306983
 
5.0%
s297756
 
4.8%
t260206
 
4.2%
u204480
 
3.3%
Other values (56)2238873
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5079293
82.3%
Uppercase Letter850304
 
13.8%
Space Separator145515
 
2.4%
Dash Punctuation86088
 
1.4%
Other Punctuation5413
 
0.1%
Open Punctuation1029
 
< 0.1%
Close Punctuation1029
 
< 0.1%
Decimal Number608
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e648027
12.8%
a564130
11.1%
r447685
 
8.8%
n438152
 
8.6%
o387285
 
7.6%
i375702
 
7.4%
l306983
 
6.0%
s297756
 
5.9%
t260206
 
5.1%
u204480
 
4.0%
Other values (16)1148887
22.6%
Uppercase Letter
ValueCountFrequency (%)
S83041
 
9.8%
M81543
 
9.6%
P72389
 
8.5%
B71112
 
8.4%
L66710
 
7.8%
C64687
 
7.6%
A52999
 
6.2%
R42362
 
5.0%
T34598
 
4.1%
H33679
 
4.0%
Other values (16)247184
29.1%
Other Punctuation
ValueCountFrequency (%)
'4677
86.4%
.582
 
10.8%
\150
 
2.8%
,3
 
0.1%
&1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0198
32.6%
2158
26.0%
1126
20.7%
395
15.6%
931
 
5.1%
Space Separator
ValueCountFrequency (%)
145515
100.0%
Dash Punctuation
ValueCountFrequency (%)
-86088
100.0%
Open Punctuation
ValueCountFrequency (%)
(1029
100.0%
Close Punctuation
ValueCountFrequency (%)
)1029
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5929597
96.1%
Common239682
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e648027
 
10.9%
a564130
 
9.5%
r447685
 
7.6%
n438152
 
7.4%
o387285
 
6.5%
i375702
 
6.3%
l306983
 
5.2%
s297756
 
5.0%
t260206
 
4.4%
u204480
 
3.4%
Other values (42)1999191
33.7%
Common
ValueCountFrequency (%)
145515
60.7%
-86088
35.9%
'4677
 
2.0%
(1029
 
0.4%
)1029
 
0.4%
.582
 
0.2%
0198
 
0.1%
2158
 
0.1%
\150
 
0.1%
1126
 
0.1%
Other values (4)130
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII6169279
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e648027
 
10.5%
a564130
 
9.1%
r447685
 
7.3%
n438152
 
7.1%
o387285
 
6.3%
i375702
 
6.1%
l306983
 
5.0%
s297756
 
4.8%
t260206
 
4.2%
u204480
 
3.3%
Other values (56)2238873
36.3%

address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1034685
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size112.5 MiB
Greece
 
92
Tsilivi (Planos) 29100 Greece
 
29
Brasschaat 2930 Belgium
 
24
Fira 84700 Greece
 
23
Sidari 49081 Greece
 
23
Other values (1034680)
1083206 

Length

Max length382
Median length47
Mean length49.73795755
Min length5

Characters and Unicode

Total characters53885954
Distinct characters387
Distinct categories23 ?
Distinct scripts7 ?
Distinct blocks13 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique995486 ?
Unique (%)91.9%

Sample

1st row10 Maison Neuve, 87510 Saint-Jouvent France
2nd row16 Place de l Eglise, 87510 Saint-Jouvent France
3rd row2 rue des Dames, 36800 Rivarennes France
4th row9 avenue Porte de la Correze 19170, 19170 Lacelle France
5th rowroute du Montseigne, 12620 Saint-Laurent-de-Levezou France

Common Values

ValueCountFrequency (%)
Greece92
 
< 0.1%
Tsilivi (Planos) 29100 Greece29
 
< 0.1%
Brasschaat 2930 Belgium24
 
< 0.1%
Fira 84700 Greece23
 
< 0.1%
Sidari 49081 Greece23
 
< 0.1%
Lindos 85107 Greece22
 
< 0.1%
Skala 85500 Greece21
 
< 0.1%
London England19
 
< 0.1%
Pefkohori 63085 Greece19
 
< 0.1%
Oia 84702 Greece19
 
< 0.1%
Other values (1034675)1083106
> 99.9%

Length

2021-12-08T01:05:11.002777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
italy224837
 
2.7%
spain157642
 
1.9%
de156353
 
1.9%
france155984
 
1.9%
via154056
 
1.9%
england144755
 
1.8%
germany115368
 
1.4%
rue77452
 
0.9%
calle63329
 
0.8%
road62905
 
0.8%
Other values (390553)6942449
84.1%

Most occurring characters

ValueCountFrequency (%)
7172662
 
13.3%
a4595436
 
8.5%
e3928085
 
7.3%
n2781382
 
5.2%
r2730516
 
5.1%
i2325435
 
4.3%
l2225854
 
4.1%
o2148462
 
4.0%
t1877320
 
3.5%
,1535166
 
2.8%
Other values (377)22565636
41.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31694793
58.8%
Space Separator7172694
 
13.3%
Decimal Number6591218
 
12.2%
Uppercase Letter6389813
 
11.9%
Other Punctuation1754765
 
3.3%
Dash Punctuation272139
 
0.5%
Other Letter3230
 
< 0.1%
Close Punctuation1827
 
< 0.1%
Open Punctuation1819
 
< 0.1%
Other Symbol1353
 
< 0.1%
Other values (13)2303
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4595436
14.5%
e3928085
12.4%
n2781382
8.8%
r2730516
8.6%
i2325435
 
7.3%
l2225854
 
7.0%
o2148462
 
6.8%
t1877320
 
5.9%
s1332502
 
4.2%
d1259565
 
4.0%
Other values (150)6490236
20.5%
Uppercase Letter
ValueCountFrequency (%)
S727811
 
11.4%
C496367
 
7.8%
P454929
 
7.1%
B406951
 
6.4%
A366394
 
5.7%
G358728
 
5.6%
R342416
 
5.4%
M329036
 
5.1%
L317771
 
5.0%
I311869
 
4.9%
Other values (107)2277541
35.6%
Other Punctuation
ValueCountFrequency (%)
,1535166
87.5%
.128766
 
7.3%
/50412
 
2.9%
'35648
 
2.0%
&2870
 
0.2%
:836
 
< 0.1%
#368
 
< 0.1%
"231
 
< 0.1%
;132
 
< 0.1%
\99
 
< 0.1%
Other values (11)237
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
01310960
19.9%
11105455
16.8%
2773861
11.7%
3645141
9.8%
4559714
8.5%
5519944
 
7.9%
6447462
 
6.8%
8438512
 
6.7%
7436993
 
6.6%
9353176
 
5.4%
Nonspacing Mark
ValueCountFrequency (%)
̈68
40.0%
́67
39.4%
̀14
 
8.2%
̌9
 
5.3%
̃5
 
2.9%
̂3
 
1.8%
̊2
 
1.2%
̧1
 
0.6%
̨1
 
0.6%
Math Symbol
ValueCountFrequency (%)
+291
74.2%
|84
 
21.4%
±7
 
1.8%
=3
 
0.8%
<2
 
0.5%
>2
 
0.5%
2
 
0.5%
~1
 
0.3%
Other Letter
ValueCountFrequency (%)
º3136
97.1%
ª89
 
2.8%
1
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
°1277
94.4%
©38
 
2.8%
21
 
1.6%
11
 
0.8%
4
 
0.3%
®1
 
0.1%
¦1
 
0.1%
Format
ValueCountFrequency (%)
78
43.1%
60
33.1%
­27
 
14.9%
8
 
4.4%
4
 
2.2%
3
 
1.7%
1
 
0.6%
Modifier Symbol
ValueCountFrequency (%)
´117
78.5%
`21
 
14.1%
¨7
 
4.7%
^2
 
1.3%
΄1
 
0.7%
˚1
 
0.7%
Control
ValueCountFrequency (%)
11
55.0%
5
25.0%
2
 
10.0%
’1
 
5.0%
–1
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
-271795
99.9%
340
 
0.1%
3
 
< 0.1%
1
 
< 0.1%
Other Number
ValueCountFrequency (%)
³10
62.5%
3
 
18.8%
2
 
12.5%
½1
 
6.2%
Close Punctuation
ValueCountFrequency (%)
)1825
99.9%
}1
 
0.1%
]1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(1812
99.6%
6
 
0.3%
[1
 
0.1%
Final Punctuation
ValueCountFrequency (%)
1235
97.3%
25
 
2.0%
»9
 
0.7%
Initial Punctuation
ValueCountFrequency (%)
31
48.4%
24
37.5%
«9
 
14.1%
Space Separator
ValueCountFrequency (%)
7172662
> 99.9%
 32
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
ʼ2
50.0%
2
50.0%
Currency Symbol
ValueCountFrequency (%)
¤1
50.0%
£1
50.0%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_32
100.0%
Line Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin38082428
70.7%
Common15797943
29.3%
Greek4994
 
< 0.1%
Cyrillic414
 
< 0.1%
Inherited170
 
< 0.1%
Hiragana4
 
< 0.1%
Han1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a4595436
 
12.1%
e3928085
 
10.3%
n2781382
 
7.3%
r2730516
 
7.2%
i2325435
 
6.1%
l2225854
 
5.8%
o2148462
 
5.6%
t1877320
 
4.9%
s1332502
 
3.5%
d1259565
 
3.3%
Other values (166)12877871
33.8%
Common
ValueCountFrequency (%)
7172662
45.4%
,1535166
 
9.7%
01310960
 
8.3%
11105455
 
7.0%
2773861
 
4.9%
3645141
 
4.1%
4559714
 
3.5%
5519944
 
3.3%
6447462
 
2.8%
8438512
 
2.8%
Other values (80)1289066
 
8.2%
Greek
ValueCountFrequency (%)
α501
 
10.0%
ο434
 
8.7%
ρ299
 
6.0%
υ266
 
5.3%
ι234
 
4.7%
ν232
 
4.6%
λ192
 
3.8%
ε185
 
3.7%
τ185
 
3.7%
ς161
 
3.2%
Other values (51)2305
46.2%
Cyrillic
ValueCountFrequency (%)
а45
 
10.9%
и33
 
8.0%
о31
 
7.5%
р30
 
7.2%
л28
 
6.8%
е23
 
5.6%
у20
 
4.8%
н17
 
4.1%
с17
 
4.1%
в17
 
4.1%
Other values (36)153
37.0%
Inherited
ValueCountFrequency (%)
̈68
40.0%
́67
39.4%
̀14
 
8.2%
̌9
 
5.3%
̃5
 
2.9%
̂3
 
1.8%
̊2
 
1.2%
̧1
 
0.6%
̨1
 
0.6%
Hiragana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII53783852
99.8%
None99634
 
0.2%
Punctuation1834
 
< 0.1%
Cyrillic414
 
< 0.1%
Diacriticals170
 
< 0.1%
Specials25
 
< 0.1%
Letterlike Symbols11
 
< 0.1%
Hiragana4
 
< 0.1%
Modifier Letters3
 
< 0.1%
Phonetic Ext2
 
< 0.1%
Other values (3)5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7172662
 
13.3%
a4595436
 
8.5%
e3928085
 
7.3%
n2781382
 
5.2%
r2730516
 
5.1%
i2325435
 
4.3%
l2225854
 
4.1%
o2148462
 
4.0%
t1877320
 
3.5%
,1535166
 
2.9%
Other values (86)22463534
41.8%
None
ValueCountFrequency (%)
ü21699
21.8%
é9424
 
9.5%
á6118
 
6.1%
í5701
 
5.7%
ß5606
 
5.6%
ä5169
 
5.2%
ö5011
 
5.0%
ó4250
 
4.3%
º3136
 
3.1%
à2817
 
2.8%
Other values (193)30703
30.8%
Punctuation
ValueCountFrequency (%)
1235
67.3%
340
 
18.5%
78
 
4.3%
60
 
3.3%
31
 
1.7%
25
 
1.4%
24
 
1.3%
8
 
0.4%
8
 
0.4%
6
 
0.3%
Other values (8)19
 
1.0%
Diacriticals
ValueCountFrequency (%)
̈68
40.0%
́67
39.4%
̀14
 
8.2%
̌9
 
5.3%
̃5
 
2.9%
̂3
 
1.8%
̊2
 
1.2%
̧1
 
0.6%
̨1
 
0.6%
Cyrillic
ValueCountFrequency (%)
а45
 
10.9%
и33
 
8.0%
о31
 
7.5%
р30
 
7.2%
л28
 
6.8%
е23
 
5.6%
у20
 
4.8%
н17
 
4.1%
с17
 
4.1%
в17
 
4.1%
Other values (36)153
37.0%
Specials
ValueCountFrequency (%)
21
84.0%
4
 
16.0%
Letterlike Symbols
ValueCountFrequency (%)
11
100.0%
Modifier Letters
ValueCountFrequency (%)
ʼ2
66.7%
˚1
33.3%
Phonetic Ext
ValueCountFrequency (%)
2
100.0%
Hiragana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%
Latin Ext Additional
ValueCountFrequency (%)
1
50.0%
1
50.0%
CJK
ValueCountFrequency (%)
1
100.0%

latitude
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct857920
Distinct (%)80.4%
Missing15790
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean46.56718192
Minimum27.64031
Maximum69.94156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:11.368982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum27.64031
5-th percentile37.3463932
Q141.90986
median46.5851
Q351.4053675
95-th percentile54.8690489
Maximum69.94156
Range42.30125
Interquartile range (IQR)9.4955075

Descriptive statistics

Standard deviation5.882611005
Coefficient of variation (CV)0.1263252523
Kurtosis-0.06035665257
Mean46.56718192
Median Absolute Deviation (MAD)4.73456
Skewness-0.2063293143
Sum49715449.38
Variance34.60511223
MonotonicityNot monotonic
2021-12-08T01:05:11.569864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.00986175
 
< 0.1%
51.12472140
 
< 0.1%
45.003227112
 
< 0.1%
52.4187972
 
< 0.1%
40.42991354
 
< 0.1%
55.6295542
 
< 0.1%
46.69848340
 
< 0.1%
40.0909537
 
< 0.1%
41.4003534
 
< 0.1%
51.1262633
 
< 0.1%
Other values (857910)1066868
98.5%
(Missing)15790
 
1.5%
ValueCountFrequency (%)
27.640311
< 0.1%
27.640531
< 0.1%
27.640571
< 0.1%
27.6409471
< 0.1%
27.6409591
< 0.1%
27.641061
< 0.1%
27.641411
< 0.1%
27.6414661
< 0.1%
27.6419891
< 0.1%
27.6996861
< 0.1%
ValueCountFrequency (%)
69.941561
< 0.1%
69.9265751
< 0.1%
69.9071661
< 0.1%
69.906551
< 0.1%
69.894531
< 0.1%
69.508261
< 0.1%
69.399881
< 0.1%
69.3991851
< 0.1%
69.399171
< 0.1%
69.251321
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct969586
Distinct (%)90.8%
Missing15790
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean5.838040356
Minimum-71.218094
Maximum33.369423
Zeros1
Zeros (%)< 0.1%
Negative335442
Negative (%)31.0%
Memory size8.3 MiB
2021-12-08T01:05:11.877177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-71.218094
5-th percentile-7.6241437
Q1-0.8027315
median5.64653
Q312.2376745
95-th percentile20.9910173
Maximum33.369423
Range104.587517
Interquartile range (IQR)13.040406

Descriptive statistics

Standard deviation8.639410037
Coefficient of variation (CV)1.479847605
Kurtosis-0.2564410915
Mean5.838040356
Median Absolute Deviation (MAD)6.529056
Skewness0.1112689221
Sum6232732.751
Variance74.63940579
MonotonicityNot monotonic
2021-12-08T01:05:12.089442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.95707175
 
< 0.1%
-2.74140
 
< 0.1%
11.083693112
 
< 0.1%
-1.24734972
 
< 0.1%
-3.66924552
 
< 0.1%
9.2010541
 
< 0.1%
2.54904740
 
< 0.1%
-3.46461837
 
< 0.1%
2.15959234
 
< 0.1%
-2.74086133
 
< 0.1%
Other values (969576)1066871
98.5%
(Missing)15790
 
1.5%
ValueCountFrequency (%)
-71.2180941
< 0.1%
-31.2655431
< 0.1%
-31.2636721
< 0.1%
-31.262591
< 0.1%
-31.2615591
< 0.1%
-31.2564331
< 0.1%
-31.2559071
< 0.1%
-31.2091831
< 0.1%
-31.1862851
< 0.1%
-31.179341
< 0.1%
ValueCountFrequency (%)
33.3694231
< 0.1%
33.287161
< 0.1%
30.9387261
< 0.1%
30.9339661
< 0.1%
30.929681
< 0.1%
30.9293731
< 0.1%
30.92911
< 0.1%
30.919311
< 0.1%
30.3374521
< 0.1%
30.3365821
< 0.1%

claimed
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing1842
Missing (%)0.2%
Memory size67.2 MiB
Unclaimed
607159 
Claimed
474396 

Length

Max length9
Median length9
Mean length8.122751964
Min length7

Characters and Unicode

Total characters8785203
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClaimed
2nd rowUnclaimed
3rd rowClaimed
4th rowClaimed
5th rowUnclaimed

Common Values

ValueCountFrequency (%)
Unclaimed607159
56.0%
Claimed474396
43.8%
(Missing)1842
 
0.2%

Length

2021-12-08T01:05:12.294070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T01:05:12.433033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
unclaimed607159
56.1%
claimed474396
43.9%

Most occurring characters

ValueCountFrequency (%)
l1081555
12.3%
a1081555
12.3%
i1081555
12.3%
m1081555
12.3%
e1081555
12.3%
d1081555
12.3%
U607159
6.9%
n607159
6.9%
c607159
6.9%
C474396
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7703648
87.7%
Uppercase Letter1081555
 
12.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l1081555
14.0%
a1081555
14.0%
i1081555
14.0%
m1081555
14.0%
e1081555
14.0%
d1081555
14.0%
n607159
7.9%
c607159
7.9%
Uppercase Letter
ValueCountFrequency (%)
U607159
56.1%
C474396
43.9%

Most occurring scripts

ValueCountFrequency (%)
Latin8785203
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l1081555
12.3%
a1081555
12.3%
i1081555
12.3%
m1081555
12.3%
e1081555
12.3%
d1081555
12.3%
U607159
6.9%
n607159
6.9%
c607159
6.9%
C474396
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII8785203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l1081555
12.3%
a1081555
12.3%
i1081555
12.3%
m1081555
12.3%
e1081555
12.3%
d1081555
12.3%
U607159
6.9%
n607159
6.9%
c607159
6.9%
C474396
5.4%

awards
Categorical

HIGH CARDINALITY
MISSING

Distinct917
Distinct (%)0.3%
Missing820264
Missing (%)75.7%
Memory size67.8 MiB
Travellers' Choice, Certificate of Excellence 2020
20868 
Certificate of Excellence 2017
 
16392
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019
 
15994
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016
 
13935
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017
 
13380
Other values (912)
182564 

Length

Max length380
Median length94
Mean length113.6036377
Min length30

Characters and Unicode

Total characters29892866
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)0.1%

Sample

1st rowTravellers' Choice, Certificate of Excellence 2020
2nd rowTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017
3rd rowTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016, Certificate of Excellence 2015
4th rowTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016, Certificate of Excellence 2015
5th rowCertificate of Excellence 2018, Certificate of Excellence 2017

Common Values

ValueCountFrequency (%)
Travellers' Choice, Certificate of Excellence 202020868
 
1.9%
Certificate of Excellence 201716392
 
1.5%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 201915994
 
1.5%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 201613935
 
1.3%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 201713380
 
1.2%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 201811988
 
1.1%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016, Certificate of Excellence 201511107
 
1.0%
Certificate of Excellence 201810011
 
0.9%
Certificate of Excellence 20199446
 
0.9%
Travellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016, Certificate of Excellence 2015, Certificate of Excellence 20148367
 
0.8%
Other values (907)131645
 
12.2%
(Missing)820264
75.7%

Length

2021-12-08T01:05:12.614310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of824284
22.0%
certificate824119
22.0%
excellence824119
22.0%
travellers147648
 
3.9%
choice147648
 
3.9%
2020144978
 
3.9%
2017142887
 
3.8%
2019139995
 
3.7%
2018138741
 
3.7%
2016107686
 
2.9%
Other values (38)306349
 
8.2%

Most occurring characters

ValueCountFrequency (%)
e4638105
15.5%
3485321
11.7%
c2664303
 
8.9%
l1998943
 
6.7%
i1880691
 
6.3%
t1694431
 
5.7%
f1676984
 
5.6%
r1178000
 
3.9%
o1056720
 
3.5%
21019844
 
3.4%
Other values (43)8599524
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20110514
67.3%
Space Separator3485321
 
11.7%
Decimal Number3384676
 
11.3%
Uppercase Letter2017106
 
6.7%
Other Punctuation895249
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4638105
23.1%
c2664303
13.2%
l1998943
9.9%
i1880691
9.4%
t1694431
 
8.4%
f1676984
 
8.3%
r1178000
 
5.9%
o1056720
 
5.3%
a1018786
 
5.1%
n881107
 
4.4%
Other values (15)1422444
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
C976604
48.4%
E824648
40.9%
T155142
 
7.7%
M31130
 
1.5%
G8978
 
0.4%
P7162
 
0.4%
S6339
 
0.3%
B2146
 
0.1%
O1672
 
0.1%
H1672
 
0.1%
Other values (2)1613
 
0.1%
Decimal Number
ValueCountFrequency (%)
21019844
30.1%
0991147
29.3%
1705526
20.8%
7142887
 
4.2%
9139995
 
4.1%
8138741
 
4.1%
6107686
 
3.2%
566340
 
2.0%
441445
 
1.2%
331065
 
0.9%
Other Punctuation
ValueCountFrequency (%)
,734418
82.0%
'147648
 
16.5%
:10982
 
1.2%
!2004
 
0.2%
.197
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3485321
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin22127620
74.0%
Common7765246
 
26.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4638105
21.0%
c2664303
12.0%
l1998943
9.0%
i1880691
8.5%
t1694431
 
7.7%
f1676984
 
7.6%
r1178000
 
5.3%
o1056720
 
4.8%
a1018786
 
4.6%
C976604
 
4.4%
Other values (27)3344053
15.1%
Common
ValueCountFrequency (%)
3485321
44.9%
21019844
 
13.1%
0991147
 
12.8%
,734418
 
9.5%
1705526
 
9.1%
'147648
 
1.9%
7142887
 
1.8%
9139995
 
1.8%
8138741
 
1.8%
6107686
 
1.4%
Other values (6)152033
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII29892866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4638105
15.5%
3485321
11.7%
c2664303
 
8.9%
l1998943
 
6.7%
i1880691
 
6.3%
t1694431
 
5.7%
f1676984
 
5.6%
r1178000
 
3.9%
o1056720
 
3.5%
21019844
 
3.4%
Other values (43)8599524
28.8%

popularity_detailed
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct981409
Distinct (%)99.3%
Missing94988
Missing (%)8.8%
Memory size89.5 MiB
#7616 of 8661 Restaurants in Barcelona
 
119
#8393 of 10193 Restaurants in Madrid
 
99
#4081 of 4632 Restaurants in Prague
 
90
#5951 of 6682 Restaurants in Milan
 
89
#15227 of 17023 Restaurants in London
 
85
Other values (981404)
987927 

Length

Max length73
Median length34
Mean length34.86049095
Min length21

Characters and Unicode

Total characters34456423
Distinct characters72
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique977358 ?
Unique (%)98.9%

Sample

1st row#1 of 2 Restaurants in Saint-Jouvent
2nd row#2 of 2 Restaurants in Saint-Jouvent
3rd row#1 of 1 Restaurant in Rivarennes
4th row#1 of 1 Restaurant in Lacelle
5th row#1 of 1 Restaurant in Saint-Laurent-de-Levezou

Common Values

ValueCountFrequency (%)
#7616 of 8661 Restaurants in Barcelona119
 
< 0.1%
#8393 of 10193 Restaurants in Madrid99
 
< 0.1%
#4081 of 4632 Restaurants in Prague90
 
< 0.1%
#5951 of 6682 Restaurants in Milan89
 
< 0.1%
#15227 of 17023 Restaurants in London85
 
< 0.1%
#9123 of 10232 Restaurants in Rome84
 
< 0.1%
#14004 of 15476 Restaurants in Paris75
 
< 0.1%
#715 of 860 Restaurants in Brno44
 
< 0.1%
#5233 of 5605 Restaurants in Berlin43
 
< 0.1%
#3443 of 3747 Restaurants in Vienna43
 
< 0.1%
Other values (981399)987638
91.2%
(Missing)94988
 
8.8%

Length

2021-12-08T01:05:13.035934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in990557
 
15.7%
of988924
 
15.7%
restaurants871237
 
13.8%
1122519
 
1.9%
278697
 
1.2%
360245
 
1.0%
449179
 
0.8%
42785
 
0.7%
542522
 
0.7%
tea40643
 
0.6%
Other values (74995)3030689
48.0%

Most occurring characters

ValueCountFrequency (%)
5329632
15.5%
a2784779
 
8.1%
n2544946
 
7.4%
s2248410
 
6.5%
t2204087
 
6.4%
e1972740
 
5.7%
o1713724
 
5.0%
i1615019
 
4.7%
r1557113
 
4.5%
u1176463
 
3.4%
Other values (62)11309510
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20990548
60.9%
Space Separator5329632
 
15.5%
Decimal Number4669826
 
13.6%
Uppercase Letter2328022
 
6.8%
Other Punctuation1051148
 
3.1%
Dash Punctuation85155
 
0.2%
Open Punctuation1046
 
< 0.1%
Close Punctuation1046
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2784779
13.3%
n2544946
12.1%
s2248410
10.7%
t2204087
10.5%
e1972740
9.4%
o1713724
8.2%
i1615019
7.7%
r1557113
7.4%
u1176463
5.6%
f1125780
5.4%
Other values (16)2047487
9.8%
Uppercase Letter
ValueCountFrequency (%)
R946791
40.7%
C149806
 
6.4%
B148594
 
6.4%
S145163
 
6.2%
M114316
 
4.9%
L103998
 
4.5%
P101273
 
4.4%
T97016
 
4.2%
A75811
 
3.3%
D55609
 
2.4%
Other values (16)389645
16.7%
Decimal Number
ValueCountFrequency (%)
1966862
20.7%
2637412
13.6%
3521278
11.2%
4442525
9.5%
5404063
8.7%
6403163
8.6%
7351050
 
7.5%
0331517
 
7.1%
8318775
 
6.8%
9293181
 
6.3%
Other Punctuation
ValueCountFrequency (%)
#988409
94.0%
&41881
 
4.0%
'10340
 
1.0%
\9919
 
0.9%
.536
 
0.1%
/63
 
< 0.1%
Space Separator
ValueCountFrequency (%)
5329632
100.0%
Dash Punctuation
ValueCountFrequency (%)
-85155
100.0%
Open Punctuation
ValueCountFrequency (%)
(1046
100.0%
Close Punctuation
ValueCountFrequency (%)
)1046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23318570
67.7%
Common11137853
32.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2784779
11.9%
n2544946
10.9%
s2248410
9.6%
t2204087
9.5%
e1972740
8.5%
o1713724
 
7.3%
i1615019
 
6.9%
r1557113
 
6.7%
u1176463
 
5.0%
f1125780
 
4.8%
Other values (42)4375509
18.8%
Common
ValueCountFrequency (%)
5329632
47.9%
#988409
 
8.9%
1966862
 
8.7%
2637412
 
5.7%
3521278
 
4.7%
4442525
 
4.0%
5404063
 
3.6%
6403163
 
3.6%
7351050
 
3.2%
0331517
 
3.0%
Other values (10)761942
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII34456423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5329632
15.5%
a2784779
 
8.1%
n2544946
 
7.4%
s2248410
 
6.5%
t2204087
 
6.4%
e1972740
 
5.7%
o1713724
 
5.0%
i1615019
 
4.7%
r1557113
 
4.5%
u1176463
 
3.4%
Other values (62)11309510
32.8%

popularity_generic
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct981940
Distinct (%)99.6%
Missing97792
Missing (%)9.0%
Memory size91.4 MiB
#1 of 1 places to eat in Agios Ioannis
 
6
#1 of 1 places to eat in Weston
 
5
#1 of 1 places to eat in Clifton
 
5
#1 of 1 places to eat in Spilia
 
4
#1 of 1 places to eat in Platanos
 
4
Other values (981935)
985581 

Length

Max length77
Median length36
Mean length37.11203373
Min length27

Characters and Unicode

Total characters36577806
Distinct characters72
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique978351 ?
Unique (%)99.3%

Sample

1st row#1 of 2 places to eat in Saint-Jouvent
2nd row#2 of 2 places to eat in Saint-Jouvent
3rd row#1 of 1 places to eat in Rivarennes
4th row#1 of 1 places to eat in Lacelle
5th row#1 of 1 places to eat in Saint-Laurent-de-Levezou

Common Values

ValueCountFrequency (%)
#1 of 1 places to eat in Agios Ioannis6
 
< 0.1%
#1 of 1 places to eat in Weston5
 
< 0.1%
#1 of 1 places to eat in Clifton5
 
< 0.1%
#1 of 1 places to eat in Spilia4
 
< 0.1%
#1 of 1 places to eat in Platanos4
 
< 0.1%
#1 of 1 places to eat in Atalaia4
 
< 0.1%
#1 of 1 places to eat in Saint-Symphorien4
 
< 0.1%
#1 of 1 places to eat in Saint-Sauveur4
 
< 0.1%
#1 of 1 places to eat in Buch4
 
< 0.1%
#1 of 1 places to eat in Baron3
 
< 0.1%
Other values (981930)985562
91.0%
(Missing)97792
 
9.0%

Length

2021-12-08T01:05:13.468824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in987740
 
12.1%
of986099
 
12.1%
places985605
 
12.1%
to985605
 
12.1%
eat985605
 
12.1%
180595
 
1.0%
259004
 
0.7%
347147
 
0.6%
440126
 
0.5%
535309
 
0.4%
Other values (77819)2947669
36.2%

Most occurring characters

ValueCountFrequency (%)
7154943
19.6%
a2915828
 
8.0%
e2845105
 
7.8%
o2632821
 
7.2%
t2321189
 
6.3%
n1647897
 
4.5%
i1557690
 
4.3%
l1467477
 
4.0%
s1375691
 
3.8%
c1167162
 
3.2%
Other values (62)11492003
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter22132755
60.5%
Space Separator7154943
 
19.6%
Decimal Number4970082
 
13.6%
Uppercase Letter1236380
 
3.4%
Other Punctuation996652
 
2.7%
Dash Punctuation84908
 
0.2%
Open Punctuation1043
 
< 0.1%
Close Punctuation1043
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2915828
13.2%
e2845105
12.9%
o2632821
11.9%
t2321189
10.5%
n1647897
7.4%
i1557690
7.0%
l1467477
6.6%
s1375691
6.2%
c1167162
 
5.3%
p1056259
 
4.8%
Other values (16)3145636
14.2%
Uppercase Letter
ValueCountFrequency (%)
S122731
 
9.9%
B115878
 
9.4%
M112984
 
9.1%
C105524
 
8.5%
L103689
 
8.4%
P99799
 
8.1%
A75593
 
6.1%
T56243
 
4.5%
R54228
 
4.4%
V51783
 
4.2%
Other values (16)337928
27.3%
Decimal Number
ValueCountFrequency (%)
1969273
19.5%
2694484
14.0%
3573121
11.5%
4504651
10.2%
5429869
8.6%
6427514
8.6%
8367854
 
7.4%
7356996
 
7.2%
0327989
 
6.6%
9318331
 
6.4%
Other Punctuation
ValueCountFrequency (%)
#985605
98.9%
'10315
 
1.0%
.536
 
0.1%
\133
 
< 0.1%
/62
 
< 0.1%
&1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7154943
100.0%
Dash Punctuation
ValueCountFrequency (%)
-84908
100.0%
Open Punctuation
ValueCountFrequency (%)
(1043
100.0%
Close Punctuation
ValueCountFrequency (%)
)1043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23369135
63.9%
Common13208671
36.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2915828
12.5%
e2845105
12.2%
o2632821
11.3%
t2321189
9.9%
n1647897
 
7.1%
i1557690
 
6.7%
l1467477
 
6.3%
s1375691
 
5.9%
c1167162
 
5.0%
p1056259
 
4.5%
Other values (42)4382016
18.8%
Common
ValueCountFrequency (%)
7154943
54.2%
#985605
 
7.5%
1969273
 
7.3%
2694484
 
5.3%
3573121
 
4.3%
4504651
 
3.8%
5429869
 
3.3%
6427514
 
3.2%
8367854
 
2.8%
7356996
 
2.7%
Other values (10)744361
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII36577806
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7154943
19.6%
a2915828
 
8.0%
e2845105
 
7.8%
o2632821
 
7.2%
t2321189
 
6.3%
n1647897
 
4.5%
i1557690
 
4.3%
l1467477
 
4.0%
s1375691
 
3.8%
c1167162
 
3.2%
Other values (62)11492003
31.4%

top_tags
Categorical

HIGH CARDINALITY
MISSING

Distinct39962
Distinct (%)4.1%
Missing110634
Missing (%)10.2%
Memory size84.0 MiB
Mid-range, French
 
20211
Mid-range
 
19422
Cheap Eats
 
15864
Mid-range, Italian
 
14363
Italian
 
14103
Other values (39957)
888800 

Length

Max length75
Median length32
Mean length29.9151448
Min length3

Characters and Unicode

Total characters29100346
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20177 ?
Unique (%)2.1%

Sample

1st rowCheap Eats, French
2nd rowCheap Eats
3rd rowCheap Eats, French, European
4th rowCheap Eats, French
5th rowMid-range, French

Common Values

ValueCountFrequency (%)
Mid-range, French20211
 
1.9%
Mid-range19422
 
1.8%
Cheap Eats15864
 
1.5%
Mid-range, Italian14363
 
1.3%
Italian14103
 
1.3%
Mid-range, Bar, British, Pub13566
 
1.3%
Mid-range, Italian, Seafood, Mediterranean13414
 
1.2%
Mid-range, Italian, Pizza, Mediterranean12562
 
1.2%
Mid-range, Italian, Pizza, Seafood12099
 
1.1%
Cafe10613
 
1.0%
Other values (39952)826546
76.3%
(Missing)110634
 
10.2%

Length

2021-12-08T01:05:13.880457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mid-range538207
 
15.2%
cheap240351
 
6.8%
eats240351
 
6.8%
italian236822
 
6.7%
european192287
 
5.4%
mediterranean155388
 
4.4%
friendly133520
 
3.8%
vegetarian133520
 
3.8%
pizza113259
 
3.2%
cafe107793
 
3.0%
Other values (200)1456411
41.0%

Most occurring characters

ValueCountFrequency (%)
a3345414
 
11.5%
e2867306
 
9.9%
2575177
 
8.8%
n2357873
 
8.1%
i2087983
 
7.2%
,1994966
 
6.9%
r1944584
 
6.7%
t1227298
 
4.2%
d1021411
 
3.5%
s817661
 
2.8%
Other values (47)8860673
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter20485901
70.4%
Uppercase Letter3495787
 
12.0%
Space Separator2575177
 
8.8%
Other Punctuation1995467
 
6.9%
Dash Punctuation548014
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a3345414
16.3%
e2867306
14.0%
n2357873
11.5%
i2087983
10.2%
r1944584
9.5%
t1227298
 
6.0%
d1021411
 
5.0%
s817661
 
4.0%
g755010
 
3.7%
o657479
 
3.2%
Other values (16)3403882
16.6%
Uppercase Letter
ValueCountFrequency (%)
M714263
20.4%
E446913
12.8%
C417197
11.9%
F339000
9.7%
I308315
8.8%
B259214
 
7.4%
S225394
 
6.4%
P206005
 
5.9%
V155389
 
4.4%
A94820
 
2.7%
Other values (16)329277
9.4%
Other Punctuation
ValueCountFrequency (%)
,1994966
> 99.9%
&495
 
< 0.1%
/6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2575177
100.0%
Dash Punctuation
ValueCountFrequency (%)
-548014
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23981688
82.4%
Common5118658
 
17.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a3345414
13.9%
e2867306
12.0%
n2357873
 
9.8%
i2087983
 
8.7%
r1944584
 
8.1%
t1227298
 
5.1%
d1021411
 
4.3%
s817661
 
3.4%
g755010
 
3.1%
M714263
 
3.0%
Other values (42)6842885
28.5%
Common
ValueCountFrequency (%)
2575177
50.3%
,1994966
39.0%
-548014
 
10.7%
&495
 
< 0.1%
/6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII29100346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a3345414
 
11.5%
e2867306
 
9.9%
2575177
 
8.8%
n2357873
 
8.1%
i2087983
 
7.2%
,1994966
 
6.9%
r1944584
 
6.7%
t1227298
 
4.2%
d1021411
 
3.5%
s817661
 
2.8%
Other values (47)8860673
30.4%

price_level
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing277205
Missing (%)25.6%
Memory size78.3 MiB
€€-€€€
537918 
240205 
€€€€
 
28069

Length

Max length6
Median length6
Mean length4.440615883
Min length1

Characters and Unicode

Total characters3579989
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row€€-€€€

Common Values

ValueCountFrequency (%)
€€-€€€537918
49.7%
240205
22.2%
€€€€28069
 
2.6%
(Missing)277205
25.6%

Length

2021-12-08T01:05:14.097260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T01:05:14.189735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
€€-€€€537918
66.7%
240205
29.8%
€€€€28069
 
3.5%

Most occurring characters

ValueCountFrequency (%)
3042071
85.0%
-537918
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Currency Symbol3042071
85.0%
Dash Punctuation537918
 
15.0%

Most frequent character per category

Currency Symbol
ValueCountFrequency (%)
3042071
100.0%
Dash Punctuation
ValueCountFrequency (%)
-537918
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3579989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3042071
85.0%
-537918
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
Currency Symbols3042071
85.0%
ASCII537918
 
15.0%

Most frequent character per block

Currency Symbols
ValueCountFrequency (%)
3042071
100.0%
ASCII
ValueCountFrequency (%)
-537918
100.0%

price_range
Categorical

HIGH CARDINALITY
MISSING

Distinct7298
Distinct (%)2.4%
Missing779070
Missing (%)71.9%
Memory size51.2 MiB
€10-€30
 
5937
€5-€15
 
5810
€10-€20
 
5148
€5-€20
 
4793
€10-€25
 
4448
Other values (7293)
278191 

Length

Max length25
Median length6
Mean length6.417379989
Min length5

Characters and Unicode

Total characters1952982
Distinct characters17
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3478 ?
Unique (%)1.1%

Sample

1st row€14-€29
2nd row€8-€17
3rd row€10-€35
4th row€12-€26
5th row€12-€30

Common Values

ValueCountFrequency (%)
€10-€305937
 
0.5%
€5-€155810
 
0.5%
€10-€205148
 
0.5%
€5-€204793
 
0.4%
€10-€254448
 
0.4%
€5-€103965
 
0.4%
€15-€303735
 
0.3%
€2-€103416
 
0.3%
€3-€103385
 
0.3%
€5-€252682
 
0.2%
Other values (7288)261008
 
24.1%
(Missing)779070
71.9%

Length

2021-12-08T01:05:14.332112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
€10-€305937
 
1.9%
€5-€155810
 
1.9%
€10-€205148
 
1.6%
€5-€204793
 
1.5%
€10-€254448
 
1.4%
chf4207
 
1.3%
€5-€103965
 
1.3%
€15-€303735
 
1.2%
€2-€103416
 
1.1%
€3-€103385
 
1.1%
Other values (6577)267897
85.7%

Most occurring characters

ValueCountFrequency (%)
600240
30.7%
-304327
15.6%
1214923
 
11.0%
2171434
 
8.8%
5144512
 
7.4%
0142594
 
7.3%
3103178
 
5.3%
462716
 
3.2%
648522
 
2.5%
846647
 
2.4%
Other values (7)113889
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1014069
51.9%
Currency Symbol600240
30.7%
Dash Punctuation304327
 
15.6%
Uppercase Letter25242
 
1.3%
Space Separator8414
 
0.4%
Other Punctuation690
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1214923
21.2%
2171434
16.9%
5144512
14.3%
0142594
14.1%
3103178
10.2%
462716
 
6.2%
648522
 
4.8%
846647
 
4.6%
746198
 
4.6%
933345
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
C8414
33.3%
H8414
33.3%
F8414
33.3%
Currency Symbol
ValueCountFrequency (%)
600240
100.0%
Dash Punctuation
ValueCountFrequency (%)
-304327
100.0%
Space Separator
ValueCountFrequency (%)
 8414
100.0%
Other Punctuation
ValueCountFrequency (%)
,690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1927740
98.7%
Latin25242
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
600240
31.1%
-304327
15.8%
1214923
 
11.1%
2171434
 
8.9%
5144512
 
7.5%
0142594
 
7.4%
3103178
 
5.4%
462716
 
3.3%
648522
 
2.5%
846647
 
2.4%
Other values (4)88647
 
4.6%
Latin
ValueCountFrequency (%)
C8414
33.3%
H8414
33.3%
F8414
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1344328
68.8%
Currency Symbols600240
30.7%
None8414
 
0.4%

Most frequent character per block

Currency Symbols
ValueCountFrequency (%)
600240
100.0%
ASCII
ValueCountFrequency (%)
-304327
22.6%
1214923
16.0%
2171434
12.8%
5144512
10.7%
0142594
10.6%
3103178
 
7.7%
462716
 
4.7%
648522
 
3.6%
846647
 
3.5%
746198
 
3.4%
Other values (5)59277
 
4.4%
None
ValueCountFrequency (%)
 8414
100.0%

meals
Categorical

HIGH CARDINALITY
MISSING

Distinct745
Distinct (%)0.1%
Missing448050
Missing (%)41.4%
Memory size59.2 MiB
Lunch, Dinner
196123 
Dinner
67459 
Breakfast, Lunch, Dinner
51749 
Lunch, Dinner, After-hours
31493 
Dinner, Lunch
27103 
Other values (740)
261420 

Length

Max length53
Median length13
Mean length18.11040738
Min length5

Characters and Unicode

Total characters11506393
Distinct characters20
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique161 ?
Unique (%)< 0.1%

Sample

1st rowLunch, Dinner
2nd rowDinner, Lunch, Drinks
3rd rowLunch, Dinner
4th rowLunch, Dinner
5th rowLunch, Dinner, Drinks

Common Values

ValueCountFrequency (%)
Lunch, Dinner196123
18.1%
Dinner67459
 
6.2%
Breakfast, Lunch, Dinner51749
 
4.8%
Lunch, Dinner, After-hours31493
 
2.9%
Dinner, Lunch27103
 
2.5%
Lunch, Dinner, Drinks23327
 
2.2%
Lunch23305
 
2.2%
Breakfast13608
 
1.3%
Breakfast, Lunch13328
 
1.2%
Breakfast, Lunch, Brunch11457
 
1.1%
Other values (735)176395
 
16.3%
(Missing)448050
41.4%

Length

2021-12-08T01:05:14.549249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dinner532366
34.7%
lunch511678
33.3%
breakfast181695
 
11.8%
drinks117450
 
7.6%
brunch101571
 
6.6%
after-hours91200
 
5.9%

Most occurring characters

ValueCountFrequency (%)
n1795431
15.6%
r1115482
9.7%
,900613
 
7.8%
900613
 
7.8%
e805261
 
7.0%
u704449
 
6.1%
h704449
 
6.1%
D649816
 
5.6%
i649816
 
5.6%
c613249
 
5.3%
Other values (10)2667214
23.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8078007
70.2%
Uppercase Letter1535960
 
13.3%
Other Punctuation900613
 
7.8%
Space Separator900613
 
7.8%
Dash Punctuation91200
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n1795431
22.2%
r1115482
13.8%
e805261
10.0%
u704449
 
8.7%
h704449
 
8.7%
i649816
 
8.0%
c613249
 
7.6%
s390345
 
4.8%
a363390
 
4.5%
k299145
 
3.7%
Other values (3)636990
 
7.9%
Uppercase Letter
ValueCountFrequency (%)
D649816
42.3%
L511678
33.3%
B283266
18.4%
A91200
 
5.9%
Other Punctuation
ValueCountFrequency (%)
,900613
100.0%
Space Separator
ValueCountFrequency (%)
900613
100.0%
Dash Punctuation
ValueCountFrequency (%)
-91200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9613967
83.6%
Common1892426
 
16.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n1795431
18.7%
r1115482
11.6%
e805261
8.4%
u704449
 
7.3%
h704449
 
7.3%
D649816
 
6.8%
i649816
 
6.8%
c613249
 
6.4%
L511678
 
5.3%
s390345
 
4.1%
Other values (7)1673991
17.4%
Common
ValueCountFrequency (%)
,900613
47.6%
900613
47.6%
-91200
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII11506393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n1795431
15.6%
r1115482
9.7%
,900613
 
7.8%
900613
 
7.8%
e805261
 
7.0%
u704449
 
6.1%
h704449
 
6.1%
D649816
 
5.6%
i649816
 
5.6%
c613249
 
5.3%
Other values (10)2667214
23.2%

cuisines
Categorical

HIGH CARDINALITY
MISSING

Distinct97741
Distinct (%)10.7%
Missing169103
Missing (%)15.6%
Memory size73.3 MiB
Italian
 
53243
French
 
39103
Cafe
 
35009
Spanish
 
27339
Italian, Pizza
 
26998
Other values (97736)
732602 

Length

Max length185
Median length17
Mean length21.11882392
Min length3

Characters and Unicode

Total characters19308814
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73786 ?
Unique (%)8.1%

Sample

1st rowFrench
2nd rowFrench, European
3rd rowFrench
4th rowFrench
5th rowFrench

Common Values

ValueCountFrequency (%)
Italian53243
 
4.9%
French39103
 
3.6%
Cafe35009
 
3.2%
Spanish27339
 
2.5%
Italian, Pizza26998
 
2.5%
French, European14323
 
1.3%
Fast food13803
 
1.3%
Bar, British, Pub13703
 
1.3%
Pizza13440
 
1.2%
German13244
 
1.2%
Other values (97731)664089
61.3%
(Missing)169103
 
15.6%

Length

2021-12-08T01:05:14.786272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
italian235823
 
9.9%
european234759
 
9.8%
mediterranean173020
 
7.2%
pizza114070
 
4.8%
cafe109188
 
4.6%
bar107990
 
4.5%
french98480
 
4.1%
spanish93191
 
3.9%
pub91258
 
3.8%
seafood81397
 
3.4%
Other values (155)1049222
43.9%

Most occurring characters

ValueCountFrequency (%)
a2358346
12.2%
e1755284
 
9.1%
n1688174
 
8.7%
1474104
 
7.6%
,1318639
 
6.8%
r1313379
 
6.8%
i1290115
 
6.7%
t930201
 
4.8%
o771444
 
4.0%
u553163
 
2.9%
Other values (43)5855965
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14116785
73.1%
Uppercase Letter2358757
 
12.2%
Space Separator1474104
 
7.6%
Other Punctuation1319146
 
6.8%
Dash Punctuation40022
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a2358346
16.7%
e1755284
12.4%
n1688174
12.0%
r1313379
9.3%
i1290115
9.1%
t930201
 
6.6%
o771444
 
5.5%
u553163
 
3.9%
s530744
 
3.8%
l496678
 
3.5%
Other values (15)2429257
17.2%
Uppercase Letter
ValueCountFrequency (%)
I339595
14.4%
S277265
11.8%
E255737
10.8%
P242532
10.3%
B236992
10.0%
C213756
9.1%
M196285
8.3%
F182448
7.7%
A110350
 
4.7%
G104079
 
4.4%
Other values (14)199718
8.5%
Other Punctuation
ValueCountFrequency (%)
,1318639
> 99.9%
&507
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1474104
100.0%
Dash Punctuation
ValueCountFrequency (%)
-40022
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16475542
85.3%
Common2833272
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a2358346
14.3%
e1755284
 
10.7%
n1688174
 
10.2%
r1313379
 
8.0%
i1290115
 
7.8%
t930201
 
5.6%
o771444
 
4.7%
u553163
 
3.4%
s530744
 
3.2%
l496678
 
3.0%
Other values (39)4788014
29.1%
Common
ValueCountFrequency (%)
1474104
52.0%
,1318639
46.5%
-40022
 
1.4%
&507
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII19308814
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a2358346
12.2%
e1755284
 
9.1%
n1688174
 
8.7%
1474104
 
7.6%
,1318639
 
6.8%
r1313379
 
6.8%
i1290115
 
6.7%
t930201
 
4.8%
o771444
 
4.0%
u553163
 
2.9%
Other values (43)5855965
30.3%

special_diets
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct68
Distinct (%)< 0.1%
Missing743141
Missing (%)68.6%
Memory size51.5 MiB
Vegetarian Friendly
156652 
Vegetarian Friendly, Vegan Options, Gluten Free Options
71379 
Vegetarian Friendly, Vegan Options
49606 
Vegetarian Friendly, Gluten Free Options
32205 
Gluten Free Options
 
9898
Other values (63)
20516 

Length

Max length70
Median length19
Mean length31.78660773
Min length5

Characters and Unicode

Total characters10815584
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowVegetarian Friendly
2nd rowVegetarian Friendly
3rd rowVegetarian Friendly
4th rowVegetarian Friendly
5th rowVegetarian Friendly

Common Values

ValueCountFrequency (%)
Vegetarian Friendly156652
 
14.5%
Vegetarian Friendly, Vegan Options, Gluten Free Options71379
 
6.6%
Vegetarian Friendly, Vegan Options49606
 
4.6%
Vegetarian Friendly, Gluten Free Options32205
 
3.0%
Gluten Free Options9898
 
0.9%
Vegetarian Friendly, Gluten Free Options, Vegan Options3875
 
0.4%
Vegan Options3660
 
0.3%
Vegan Options, Vegetarian Friendly2034
 
0.2%
Halal1730
 
0.2%
Vegetarian Friendly, Vegan Options, Halal, Gluten Free Options1706
 
0.2%
Other values (58)7511
 
0.7%
(Missing)743141
68.6%

Length

2021-12-08T01:05:15.033771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vegetarian324017
24.9%
friendly324017
24.9%
options260094
20.0%
vegan136597
10.5%
gluten123497
 
9.5%
free123497
 
9.5%
halal6709
 
0.5%
kosher298
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e1479437
13.7%
n1168222
10.8%
958470
 
8.9%
i908128
 
8.4%
a798049
 
7.4%
r771829
 
7.1%
t707608
 
6.5%
l460932
 
4.3%
V460614
 
4.3%
g460614
 
4.3%
Other values (13)2641681
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8307526
76.8%
Uppercase Letter1298726
 
12.0%
Space Separator958470
 
8.9%
Other Punctuation250862
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1479437
17.8%
n1168222
14.1%
i908128
10.9%
a798049
9.6%
r771829
9.3%
t707608
8.5%
l460932
 
5.5%
g460614
 
5.5%
d324017
 
3.9%
y324017
 
3.9%
Other values (5)904673
10.9%
Uppercase Letter
ValueCountFrequency (%)
V460614
35.5%
F447514
34.5%
O260094
20.0%
G123497
 
9.5%
H6709
 
0.5%
K298
 
< 0.1%
Space Separator
ValueCountFrequency (%)
958470
100.0%
Other Punctuation
ValueCountFrequency (%)
,250862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9606252
88.8%
Common1209332
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1479437
15.4%
n1168222
12.2%
i908128
9.5%
a798049
8.3%
r771829
 
8.0%
t707608
 
7.4%
l460932
 
4.8%
V460614
 
4.8%
g460614
 
4.8%
F447514
 
4.7%
Other values (11)1943305
20.2%
Common
ValueCountFrequency (%)
958470
79.3%
,250862
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10815584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1479437
13.7%
n1168222
10.8%
958470
 
8.9%
i908128
 
8.4%
a798049
 
7.4%
r771829
 
7.1%
t707608
 
6.5%
l460932
 
4.3%
V460614
 
4.3%
g460614
 
4.3%
Other values (13)2641681
24.4%

features
Categorical

HIGH CARDINALITY
MISSING

Distinct56453
Distinct (%)17.8%
Missing765990
Missing (%)70.7%
Memory size61.5 MiB
Reservations
36514 
Reservations, Seating, Table Service
 
15193
Takeout
 
7290
Reservations, Seating, Serves Alcohol, Table Service
 
7181
Wheelchair Accessible
 
5863
Other values (56448)
245366 

Length

Max length588
Median length52
Mean length68.95317684
Min length4

Characters and Unicode

Total characters21886221
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48360 ?
Unique (%)15.2%

Sample

1st rowReservations, Seating, Wheelchair Accessible, Serves Alcohol, Accepts Credit Cards, Table Service
2nd rowReservations, Seating, Table Service, Wheelchair Accessible
3rd rowReservations, Seating, Serves Alcohol, Table Service, Wheelchair Accessible
4th rowReservations, Seating, Wheelchair Accessible, Table Service
5th rowReservations, Seating, Table Service, Serves Alcohol

Common Values

ValueCountFrequency (%)
Reservations36514
 
3.4%
Reservations, Seating, Table Service15193
 
1.4%
Takeout7290
 
0.7%
Reservations, Seating, Serves Alcohol, Table Service7181
 
0.7%
Wheelchair Accessible5863
 
0.5%
Seating5815
 
0.5%
Takeout, Wheelchair Accessible5591
 
0.5%
Reservations, Seating, Wheelchair Accessible, Table Service5352
 
0.5%
Seating, Table Service5097
 
0.5%
Reservations, Seating, Wheelchair Accessible, Serves Alcohol, Table Service4446
 
0.4%
Other values (56443)219065
 
20.2%
(Missing)765990
70.7%

Length

2021-12-08T01:05:15.259330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seating302951
 
12.0%
reservations215387
 
8.5%
table191467
 
7.6%
service191467
 
7.6%
wheelchair146385
 
5.8%
accessible146385
 
5.8%
alcohol129553
 
5.1%
serves129553
 
5.1%
accepts101329
 
4.0%
takeout94983
 
3.7%
Other values (49)885559
34.9%

Most occurring characters

ValueCountFrequency (%)
e2885400
13.2%
2217612
 
10.1%
i1576833
 
7.2%
a1501036
 
6.9%
r1241264
 
5.7%
s1187791
 
5.4%
,1187317
 
5.4%
l1092206
 
5.0%
c1046080
 
4.8%
t976838
 
4.5%
Other values (36)6973844
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter15987258
73.0%
Uppercase Letter2479083
 
11.3%
Space Separator2217612
 
10.1%
Other Punctuation1187317
 
5.4%
Dash Punctuation14951
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2885400
18.0%
i1576833
9.9%
a1501036
9.4%
r1241264
7.8%
s1187791
 
7.4%
l1092206
 
6.8%
c1046080
 
6.5%
t976838
 
6.1%
o763497
 
4.8%
n700102
 
4.4%
Other values (13)3016211
18.9%
Uppercase Letter
ValueCountFrequency (%)
S638443
25.8%
A471242
19.0%
T304790
12.3%
R215387
 
8.7%
W215063
 
8.7%
C128391
 
5.2%
F112828
 
4.6%
B81191
 
3.3%
O81052
 
3.3%
P67562
 
2.7%
Other values (10)163134
 
6.6%
Space Separator
ValueCountFrequency (%)
2217612
100.0%
Other Punctuation
ValueCountFrequency (%)
,1187317
100.0%
Dash Punctuation
ValueCountFrequency (%)
-14951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18466341
84.4%
Common3419880
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2885400
15.6%
i1576833
 
8.5%
a1501036
 
8.1%
r1241264
 
6.7%
s1187791
 
6.4%
l1092206
 
5.9%
c1046080
 
5.7%
t976838
 
5.3%
o763497
 
4.1%
n700102
 
3.8%
Other values (33)5495294
29.8%
Common
ValueCountFrequency (%)
2217612
64.8%
,1187317
34.7%
-14951
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII21886221
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2885400
13.2%
2217612
 
10.1%
i1576833
 
7.2%
a1501036
 
6.9%
r1241264
 
5.7%
s1187791
 
5.4%
,1187317
 
5.4%
l1092206
 
5.0%
c1046080
 
4.8%
t976838
 
4.5%
Other values (36)6973844
31.9%

vegetarian_friendly
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
False
759380 
True
324017 
ValueCountFrequency (%)
False759380
70.1%
True324017
29.9%
2021-12-08T01:05:15.394623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

vegan_options
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
False
946800 
True
136597 
ValueCountFrequency (%)
False946800
87.4%
True136597
 
12.6%
2021-12-08T01:05:15.447086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

gluten_free
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
False
959900 
True
123497 
ValueCountFrequency (%)
False959900
88.6%
True123497
 
11.4%
2021-12-08T01:05:15.503678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

original_open_hours
Categorical

HIGH CARDINALITY
MISSING

Distinct237890
Distinct (%)40.1%
Missing489565
Missing (%)45.2%
Memory size148.5 MiB
{"Mon": ["00:00-23:59"], "Tue": ["00:00-23:59"], "Wed": ["00:00-23:59"], "Thu": ["00:00-23:59"], "Fri": ["00:00-23:59"], "Sat": ["00:00-23:59"], "Sun": ["00:00-23:59"]}
 
7674
{"Mon": ["11:00-23:00"], "Tue": ["11:00-23:00"], "Wed": ["11:00-23:00"], "Thu": ["11:00-23:00"], "Fri": ["11:00-23:00"], "Sat": ["11:00-23:00"], "Sun": ["11:00-23:00"]}
 
5303
{"Mon": ["12:00-22:00"], "Tue": ["12:00-22:00"], "Wed": ["12:00-22:00"], "Thu": ["12:00-22:00"], "Fri": ["12:00-22:00"], "Sat": ["12:00-22:00"], "Sun": ["12:00-22:00"]}
 
4234
{"Mon": ["12:00-23:00"], "Tue": ["12:00-23:00"], "Wed": ["12:00-23:00"], "Thu": ["12:00-23:00"], "Fri": ["12:00-23:00"], "Sat": ["12:00-23:00"], "Sun": ["12:00-23:00"]}
 
3677
{"Mon": ["12:00-00:00"], "Tue": ["12:00-00:00"], "Wed": ["12:00-00:00"], "Thu": ["12:00-00:00"], "Fri": ["12:00-00:00"], "Sat": ["12:00-00:00"], "Sun": ["12:00-00:00"]}
 
3673
Other values (237885)
569271 

Length

Max length292
Median length168
Mean length178.8071508
Min length90

Characters and Unicode

Total characters106181408
Distinct characters34
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198640 ?
Unique (%)33.5%

Sample

1st row{"Mon": ["09:00-14:30"], "Tue": ["09:00-14:30", "19:00-21:30"], "Wed": ["09:00-14:30", "19:00-21:30"], "Thu": ["09:00-14:30", "19:00-21:30"], "Fri": ["09:00-14:30", "19:00-22:00"], "Sat": ["09:00-14:30", "19:00-22:00"], "Sun": ["09:00-16:00"]}
2nd row{"Mon": [], "Tue": [], "Wed": ["12:00-14:30", "18:30-22:00"], "Thu": ["12:00-14:30", "18:30-22:00"], "Fri": ["12:00-14:30", "18:30-22:00"], "Sat": ["12:00-14:30", "18:30-22:00"], "Sun": ["12:00-14:30", "18:30-22:00"]}
3rd row{"Mon": [], "Tue": ["10:00-14:00"], "Wed": ["10:00-14:00"], "Thu": ["10:00-14:00"], "Fri": ["10:00-14:00"], "Sat": ["10:00-14:00"], "Sun": ["10:00-14:00"]}
4th row{"Mon": [], "Tue": [], "Wed": ["12:00-14:00"], "Thu": ["12:00-14:00"], "Fri": ["12:00-14:00", "19:00-21:00"], "Sat": ["19:00-21:00"], "Sun": ["12:00-14:00", "19:00-21:00"]}
5th row{"Mon": [], "Tue": ["09:00-16:00"], "Wed": ["09:00-16:00"], "Thu": ["09:00-21:00"], "Fri": ["09:00-21:00"], "Sat": ["16:45-23:45"], "Sun": ["09:00-17:00"]}

Common Values

ValueCountFrequency (%)
{"Mon": ["00:00-23:59"], "Tue": ["00:00-23:59"], "Wed": ["00:00-23:59"], "Thu": ["00:00-23:59"], "Fri": ["00:00-23:59"], "Sat": ["00:00-23:59"], "Sun": ["00:00-23:59"]}7674
 
0.7%
{"Mon": ["11:00-23:00"], "Tue": ["11:00-23:00"], "Wed": ["11:00-23:00"], "Thu": ["11:00-23:00"], "Fri": ["11:00-23:00"], "Sat": ["11:00-23:00"], "Sun": ["11:00-23:00"]}5303
 
0.5%
{"Mon": ["12:00-22:00"], "Tue": ["12:00-22:00"], "Wed": ["12:00-22:00"], "Thu": ["12:00-22:00"], "Fri": ["12:00-22:00"], "Sat": ["12:00-22:00"], "Sun": ["12:00-22:00"]}4234
 
0.4%
{"Mon": ["12:00-23:00"], "Tue": ["12:00-23:00"], "Wed": ["12:00-23:00"], "Thu": ["12:00-23:00"], "Fri": ["12:00-23:00"], "Sat": ["12:00-23:00"], "Sun": ["12:00-23:00"]}3677
 
0.3%
{"Mon": ["12:00-00:00"], "Tue": ["12:00-00:00"], "Wed": ["12:00-00:00"], "Thu": ["12:00-00:00"], "Fri": ["12:00-00:00"], "Sat": ["12:00-00:00"], "Sun": ["12:00-00:00"]}3673
 
0.3%
{"Mon": ["11:00-22:00"], "Tue": ["11:00-22:00"], "Wed": ["11:00-22:00"], "Thu": ["11:00-22:00"], "Fri": ["11:00-22:00"], "Sat": ["11:00-22:00"], "Sun": ["11:00-22:00"]}3299
 
0.3%
{"Mon": ["10:00-22:00"], "Tue": ["10:00-22:00"], "Wed": ["10:00-22:00"], "Thu": ["10:00-22:00"], "Fri": ["10:00-22:00"], "Sat": ["10:00-22:00"], "Sun": ["10:00-22:00"]}2632
 
0.2%
{"Mon": ["11:00-00:00"], "Tue": ["11:00-00:00"], "Wed": ["11:00-00:00"], "Thu": ["11:00-00:00"], "Fri": ["11:00-00:00"], "Sat": ["11:00-00:00"], "Sun": ["11:00-00:00"]}2405
 
0.2%
{"Mon": ["09:00-00:00"], "Tue": ["09:00-00:00"], "Wed": ["09:00-00:00"], "Thu": ["09:00-00:00"], "Fri": ["09:00-00:00"], "Sat": ["09:00-00:00"], "Sun": ["09:00-00:00"]}2329
 
0.2%
{"Mon": ["09:00-23:00"], "Tue": ["09:00-23:00"], "Wed": ["09:00-23:00"], "Thu": ["09:00-23:00"], "Fri": ["09:00-23:00"], "Sat": ["09:00-23:00"], "Sun": ["09:00-23:00"]}2184
 
0.2%
Other values (237880)556422
51.4%
(Missing)489565
45.2%

Length

2021-12-08T01:05:15.750123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mon593832
 
6.5%
tue593832
 
6.5%
wed593832
 
6.5%
thu593832
 
6.5%
fri593832
 
6.5%
sat593832
 
6.5%
sun593832
 
6.5%
399601
 
4.4%
12:00-15:00111669
 
1.2%
12:00-14:00107585
 
1.2%
Other values (5282)4312132
47.4%

Most occurring characters

ValueCountFrequency (%)
018744680
17.7%
"17376420
16.4%
:13219596
12.5%
8493979
8.0%
15962565
 
5.6%
-4531386
 
4.3%
,4337155
 
4.1%
]4156824
 
3.9%
[4156824
 
3.9%
24138667
 
3.9%
Other values (24)21063312
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36251088
34.1%
Other Punctuation34933171
32.9%
Space Separator8493979
 
8.0%
Lowercase Letter8313648
 
7.8%
Close Punctuation4750656
 
4.5%
Open Punctuation4750656
 
4.5%
Dash Punctuation4531386
 
4.3%
Uppercase Letter4156824
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018744680
51.7%
15962565
 
16.4%
24138667
 
11.4%
33167626
 
8.7%
91007849
 
2.8%
8766051
 
2.1%
7716489
 
2.0%
5695343
 
1.9%
4626401
 
1.7%
6425417
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
u1781496
21.4%
n1187664
14.3%
e1187664
14.3%
h593832
 
7.1%
r593832
 
7.1%
i593832
 
7.1%
d593832
 
7.1%
a593832
 
7.1%
t593832
 
7.1%
o593832
 
7.1%
Uppercase Letter
ValueCountFrequency (%)
S1187664
28.6%
T1187664
28.6%
F593832
14.3%
W593832
14.3%
M593832
14.3%
Other Punctuation
ValueCountFrequency (%)
"17376420
49.7%
:13219596
37.8%
,4337155
 
12.4%
Close Punctuation
ValueCountFrequency (%)
]4156824
87.5%
}593832
 
12.5%
Open Punctuation
ValueCountFrequency (%)
[4156824
87.5%
{593832
 
12.5%
Space Separator
ValueCountFrequency (%)
8493979
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4531386
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common93710936
88.3%
Latin12470472
 
11.7%

Most frequent character per script

Common
ValueCountFrequency (%)
018744680
20.0%
"17376420
18.5%
:13219596
14.1%
8493979
9.1%
15962565
 
6.4%
-4531386
 
4.8%
,4337155
 
4.6%
]4156824
 
4.4%
[4156824
 
4.4%
24138667
 
4.4%
Other values (9)8592840
9.2%
Latin
ValueCountFrequency (%)
u1781496
14.3%
S1187664
 
9.5%
n1187664
 
9.5%
T1187664
 
9.5%
e1187664
 
9.5%
h593832
 
4.8%
F593832
 
4.8%
r593832
 
4.8%
i593832
 
4.8%
d593832
 
4.8%
Other values (5)2969160
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII106181408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018744680
17.7%
"17376420
16.4%
:13219596
12.5%
8493979
8.0%
15962565
 
5.6%
-4531386
 
4.3%
,4337155
 
4.1%
]4156824
 
3.9%
[4156824
 
3.9%
24138667
 
3.9%
Other values (24)21063312
19.8%

open_days_per_week
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing489565
Missing (%)45.2%
Infinite0
Infinite (%)0.0%
Mean6.327080723
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:15.938785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q16
median7
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9667343474
Coefficient of variation (CV)0.152793111
Kurtosis6.671706407
Mean6.327080723
Median Absolute Deviation (MAD)0
Skewness-2.178990536
Sum3757223
Variance0.9345752985
MonotonicityNot monotonic
2021-12-08T01:05:16.050812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7323729
29.9%
6187868
 
17.3%
558202
 
5.4%
410127
 
0.9%
37406
 
0.7%
23676
 
0.3%
12824
 
0.3%
(Missing)489565
45.2%
ValueCountFrequency (%)
12824
 
0.3%
23676
 
0.3%
37406
 
0.7%
410127
 
0.9%
558202
 
5.4%
6187868
17.3%
7323729
29.9%
ValueCountFrequency (%)
7323729
29.9%
6187868
17.3%
558202
 
5.4%
410127
 
0.9%
37406
 
0.7%
23676
 
0.3%
12824
 
0.3%

open_hours_per_week
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3105
Distinct (%)0.5%
Missing489565
Missing (%)45.2%
Infinite0
Infinite (%)0.0%
Mean62.02328217
Minimum0
Maximum168
Zeros1479
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:16.257060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q139
median58.5
Q381.5
95-th percentile113.5
Maximum168
Range168
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation30.53813355
Coefficient of variation (CV)0.4923656485
Kurtosis0.8317549838
Mean62.02328217
Median Absolute Deviation (MAD)20.5
Skewness0.7457016458
Sum36831409.7
Variance932.5776009
MonotonicityNot monotonic
2021-12-08T01:05:16.453361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8419158
 
1.8%
4215362
 
1.4%
7712486
 
1.2%
7012228
 
1.1%
5610711
 
1.0%
6310552
 
1.0%
309960
 
0.9%
369705
 
0.9%
919150
 
0.8%
499132
 
0.8%
Other values (3095)475388
43.9%
(Missing)489565
45.2%
ValueCountFrequency (%)
01479
0.1%
0.016666666675
 
< 0.1%
0.033333333331
 
< 0.1%
0.11666666673
 
< 0.1%
0.16666666671
 
< 0.1%
0.23333333333
 
< 0.1%
0.25443
 
< 0.1%
0.575
 
< 0.1%
0.58333333331
 
< 0.1%
0.66666666671
 
< 0.1%
ValueCountFrequency (%)
1685
 
< 0.1%
167.91
 
< 0.1%
167.88333337703
0.7%
167.766666721
 
< 0.1%
167.655
 
< 0.1%
167.51
 
< 0.1%
167.41666671
 
< 0.1%
167.43
 
< 0.1%
167.18333332
 
< 0.1%
167.06666671
 
< 0.1%

working_shifts_per_week
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct15
Distinct (%)< 0.1%
Missing489565
Missing (%)45.2%
Infinite0
Infinite (%)0.0%
Mean7.630754153
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:16.640969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q16
median7
Q37
95-th percentile14
Maximum15
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.550127909
Coefficient of variation (CV)0.3341908096
Kurtosis0.7213945519
Mean7.630754153
Median Absolute Deviation (MAD)1
Skewness1.050544374
Sum4531386
Variance6.50315235
MonotonicityNot monotonic
2021-12-08T01:05:16.770076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
7269200
24.8%
6120076
 
11.1%
539570
 
3.7%
1237983
 
3.5%
1430482
 
2.8%
1122390
 
2.1%
1019618
 
1.8%
1311757
 
1.1%
811331
 
1.0%
910837
 
1.0%
Other values (5)20588
 
1.9%
(Missing)489565
45.2%
ValueCountFrequency (%)
12751
 
0.3%
23417
 
0.3%
36051
 
0.6%
48239
 
0.8%
539570
 
3.7%
6120076
11.1%
7269200
24.8%
811331
 
1.0%
910837
 
1.0%
1019618
 
1.8%
ValueCountFrequency (%)
15130
 
< 0.1%
1430482
 
2.8%
1311757
 
1.1%
1237983
 
3.5%
1122390
 
2.1%
1019618
 
1.8%
910837
 
1.0%
811331
 
1.0%
7269200
24.8%
6120076
11.1%

avg_rating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)< 0.1%
Missing96636
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean4.035942847
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:16.917373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q13.5
median4
Q34.5
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.713694003
Coefficient of variation (CV)0.1768345167
Kurtosis2.071824575
Mean4.035942847
Median Absolute Deviation (MAD)0.5
Skewness-1.13780692
Sum3982511
Variance0.5093591299
MonotonicityNot monotonic
2021-12-08T01:05:17.070344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4296979
27.4%
4.5293794
27.1%
3.5147287
13.6%
5128107
11.8%
370326
 
6.5%
2.526565
 
2.5%
213456
 
1.2%
16381
 
0.6%
1.53866
 
0.4%
(Missing)96636
 
8.9%
ValueCountFrequency (%)
16381
 
0.6%
1.53866
 
0.4%
213456
 
1.2%
2.526565
 
2.5%
370326
 
6.5%
3.5147287
13.6%
4296979
27.4%
4.5293794
27.1%
5128107
11.8%
ValueCountFrequency (%)
5128107
11.8%
4.5293794
27.1%
4296979
27.4%
3.5147287
13.6%
370326
 
6.5%
2.526565
 
2.5%
213456
 
1.2%
1.53866
 
0.4%
16381
 
0.6%

total_reviews_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct3363
Distinct (%)0.3%
Missing52235
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean102.8889893
Minimum0
Maximum52404
Zeros44149
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:17.312489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median24
Q393
95-th percentile459
Maximum52404
Range52404
Interquartile range (IQR)87

Descriptive statistics

Standard deviation267.2414795
Coefficient of variation (CV)2.597376855
Kurtosis2604.571173
Mean102.8889893
Median Absolute Deviation (MAD)22
Skewness25.28240244
Sum106095216
Variance71418.00837
MonotonicityNot monotonic
2021-12-08T01:05:17.782353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161914
 
5.7%
246078
 
4.3%
044149
 
4.1%
337374
 
3.4%
432204
 
3.0%
528324
 
2.6%
625174
 
2.3%
722880
 
2.1%
820737
 
1.9%
918983
 
1.8%
Other values (3353)693345
64.0%
(Missing)52235
 
4.8%
ValueCountFrequency (%)
044149
4.1%
161914
5.7%
246078
4.3%
337374
3.4%
432204
3.0%
528324
2.6%
625174
2.3%
722880
 
2.1%
820737
 
1.9%
918983
 
1.8%
ValueCountFrequency (%)
524041
< 0.1%
337311
< 0.1%
311441
< 0.1%
301421
< 0.1%
292731
< 0.1%
246711
< 0.1%
223641
< 0.1%
198561
< 0.1%
191671
< 0.1%
189711
< 0.1%

default_language
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing95193
Missing (%)8.8%
Memory size64.9 MiB
English
689754 
All languages
298450 

Length

Max length13
Median length7
Mean length8.81207524
Min length7

Characters and Unicode

Total characters8708128
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnglish
2nd rowAll languages
3rd rowEnglish
4th rowEnglish
5th rowAll languages

Common Values

ValueCountFrequency (%)
English689754
63.7%
All languages298450
27.5%
(Missing)95193
 
8.8%

Length

2021-12-08T01:05:17.985069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-08T01:05:18.167474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
english689754
53.6%
all298450
23.2%
languages298450
23.2%

Most occurring characters

ValueCountFrequency (%)
l1585104
18.2%
g1286654
14.8%
n988204
11.3%
s988204
11.3%
E689754
7.9%
i689754
7.9%
h689754
7.9%
a596900
 
6.9%
A298450
 
3.4%
298450
 
3.4%
Other values (2)596900
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7421474
85.2%
Uppercase Letter988204
 
11.3%
Space Separator298450
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l1585104
21.4%
g1286654
17.3%
n988204
13.3%
s988204
13.3%
i689754
9.3%
h689754
9.3%
a596900
 
8.0%
u298450
 
4.0%
e298450
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
E689754
69.8%
A298450
30.2%
Space Separator
ValueCountFrequency (%)
298450
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8409678
96.6%
Common298450
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l1585104
18.8%
g1286654
15.3%
n988204
11.8%
s988204
11.8%
E689754
8.2%
i689754
8.2%
h689754
8.2%
a596900
 
7.1%
A298450
 
3.5%
u298450
 
3.5%
Common
ValueCountFrequency (%)
298450
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8708128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l1585104
18.2%
g1286654
14.8%
n988204
11.3%
s988204
11.3%
E689754
7.9%
i689754
7.9%
h689754
7.9%
a596900
 
6.9%
A298450
 
3.4%
298450
 
3.4%
Other values (2)596900
 
6.9%

reviews_count_in_default_language
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2415
Distinct (%)0.2%
Missing95193
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean44.56341504
Minimum1
Maximum15229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:18.384275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q326
95-th percentile205
Maximum15229
Range15228
Interquartile range (IQR)24

Descriptive statistics

Standard deviation148.7281789
Coefficient of variation (CV)3.337450209
Kurtosis586.0725255
Mean44.56341504
Median Absolute Deviation (MAD)6
Skewness14.3963658
Sum44037745
Variance22120.07119
MonotonicityNot monotonic
2021-12-08T01:05:18.709614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1173914
16.1%
2101830
 
9.4%
370931
 
6.5%
454144
 
5.0%
543513
 
4.0%
635727
 
3.3%
730477
 
2.8%
826086
 
2.4%
922586
 
2.1%
1020019
 
1.8%
Other values (2405)408977
37.7%
(Missing)95193
 
8.8%
ValueCountFrequency (%)
1173914
16.1%
2101830
9.4%
370931
6.5%
454144
 
5.0%
543513
 
4.0%
635727
 
3.3%
730477
 
2.8%
826086
 
2.4%
922586
 
2.1%
1020019
 
1.8%
ValueCountFrequency (%)
152291
< 0.1%
147171
< 0.1%
137161
< 0.1%
119971
< 0.1%
92391
< 0.1%
88451
< 0.1%
85761
< 0.1%
84581
< 0.1%
83371
< 0.1%
82241
< 0.1%

excellent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1708
Distinct (%)0.2%
Missing95193
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean24.65343998
Minimum0
Maximum9383
Zeros146592
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:18.950668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q313
95-th percentile113
Maximum9383
Range9383
Interquartile range (IQR)12

Descriptive statistics

Standard deviation89.85080105
Coefficient of variation (CV)3.644554315
Kurtosis541.9635977
Mean24.65343998
Median Absolute Deviation (MAD)3
Skewness14.78952065
Sum24362628
Variance8073.16645
MonotonicityNot monotonic
2021-12-08T01:05:19.184433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1186991
17.3%
0146592
13.5%
2102840
 
9.5%
368452
 
6.3%
450414
 
4.7%
539200
 
3.6%
631461
 
2.9%
725848
 
2.4%
821891
 
2.0%
918898
 
1.7%
Other values (1698)295617
27.3%
(Missing)95193
 
8.8%
ValueCountFrequency (%)
0146592
13.5%
1186991
17.3%
2102840
9.5%
368452
 
6.3%
450414
 
4.7%
539200
 
3.6%
631461
 
2.9%
725848
 
2.4%
821891
 
2.0%
918898
 
1.7%
ValueCountFrequency (%)
93831
< 0.1%
75581
< 0.1%
72821
< 0.1%
68131
< 0.1%
53691
< 0.1%
51101
< 0.1%
49121
< 0.1%
48411
< 0.1%
47901
< 0.1%
47671
< 0.1%

very_good
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct832
Distinct (%)0.1%
Missing95193
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean10.49051613
Minimum0
Maximum4091
Zeros278879
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:19.642966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile48
Maximum4091
Range4091
Interquartile range (IQR)6

Descriptive statistics

Standard deviation35.51225601
Coefficient of variation (CV)3.385177199
Kurtosis734.0962925
Mean10.49051613
Median Absolute Deviation (MAD)2
Skewness15.87200946
Sum10366770
Variance1261.120327
MonotonicityNot monotonic
2021-12-08T01:05:20.236333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0278879
25.7%
1199272
18.4%
2100393
 
9.3%
363244
 
5.8%
444246
 
4.1%
533394
 
3.1%
626120
 
2.4%
720995
 
1.9%
817244
 
1.6%
914702
 
1.4%
Other values (822)189715
17.5%
(Missing)95193
 
8.8%
ValueCountFrequency (%)
0278879
25.7%
1199272
18.4%
2100393
 
9.3%
363244
 
5.8%
444246
 
4.1%
533394
 
3.1%
626120
 
2.4%
720995
 
1.9%
817244
 
1.6%
914702
 
1.4%
ValueCountFrequency (%)
40911
< 0.1%
35031
< 0.1%
34831
< 0.1%
29641
< 0.1%
25311
< 0.1%
24871
< 0.1%
23771
< 0.1%
22471
< 0.1%
20161
< 0.1%
19401
< 0.1%

average
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct458
Distinct (%)< 0.1%
Missing95193
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean4.10930233
Minimum0
Maximum2132
Zeros493840
Zeros (%)45.6%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:20.924443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile19
Maximum2132
Range2132
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.66963091
Coefficient of variation (CV)3.813209555
Kurtosis1413.191175
Mean4.10930233
Median Absolute Deviation (MAD)1
Skewness21.42675175
Sum4060829
Variance245.5373328
MonotonicityNot monotonic
2021-12-08T01:05:21.462911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0493840
45.6%
1178020
 
16.4%
278321
 
7.2%
345446
 
4.2%
430200
 
2.8%
521577
 
2.0%
616526
 
1.5%
713032
 
1.2%
810435
 
1.0%
98992
 
0.8%
Other values (448)91815
 
8.5%
(Missing)95193
 
8.8%
ValueCountFrequency (%)
0493840
45.6%
1178020
 
16.4%
278321
 
7.2%
345446
 
4.2%
430200
 
2.8%
521577
 
2.0%
616526
 
1.5%
713032
 
1.2%
810435
 
1.0%
98992
 
0.8%
ValueCountFrequency (%)
21321
< 0.1%
21091
< 0.1%
16821
< 0.1%
15141
< 0.1%
13321
< 0.1%
13221
< 0.1%
12451
< 0.1%
11031
< 0.1%
10871
< 0.1%
10231
< 0.1%

poor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct305
Distinct (%)< 0.1%
Missing95193
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean2.355306192
Minimum0
Maximum1253
Zeros614652
Zeros (%)56.7%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:22.044740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11
Maximum1253
Range1253
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.352756334
Coefficient of variation (CV)3.970930135
Kurtosis1078.095036
Mean2.355306192
Median Absolute Deviation (MAD)0
Skewness18.43930313
Sum2327523
Variance87.47405105
MonotonicityNot monotonic
2021-12-08T01:05:22.610441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0614652
56.7%
1147712
 
13.6%
261499
 
5.7%
334639
 
3.2%
422862
 
2.1%
516294
 
1.5%
611895
 
1.1%
79247
 
0.9%
87596
 
0.7%
96274
 
0.6%
Other values (295)55534
 
5.1%
(Missing)95193
 
8.8%
ValueCountFrequency (%)
0614652
56.7%
1147712
 
13.6%
261499
 
5.7%
334639
 
3.2%
422862
 
2.1%
516294
 
1.5%
611895
 
1.1%
79247
 
0.9%
87596
 
0.7%
96274
 
0.6%
ValueCountFrequency (%)
12531
< 0.1%
10581
< 0.1%
9911
< 0.1%
9751
< 0.1%
8561
< 0.1%
6661
< 0.1%
5941
< 0.1%
5251
< 0.1%
5161
< 0.1%
5061
< 0.1%

terrible
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct353
Distinct (%)< 0.1%
Missing95193
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean2.954850416
Minimum0
Maximum1215
Zeros573943
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:23.263685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile14
Maximum1215
Range1215
Interquartile range (IQR)2

Descriptive statistics

Standard deviation11.03006882
Coefficient of variation (CV)3.732868764
Kurtosis619.1381812
Mean2.954850416
Median Absolute Deviation (MAD)0
Skewness15.37001589
Sum2919995
Variance121.6624181
MonotonicityNot monotonic
2021-12-08T01:05:23.829217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0573943
53.0%
1149446
 
13.8%
265255
 
6.0%
339030
 
3.6%
426198
 
2.4%
518811
 
1.7%
614642
 
1.4%
711719
 
1.1%
89503
 
0.9%
97903
 
0.7%
Other values (343)71754
 
6.6%
(Missing)95193
 
8.8%
ValueCountFrequency (%)
0573943
53.0%
1149446
 
13.8%
265255
 
6.0%
339030
 
3.6%
426198
 
2.4%
518811
 
1.7%
614642
 
1.4%
711719
 
1.1%
89503
 
0.9%
97903
 
0.7%
ValueCountFrequency (%)
12151
< 0.1%
10591
< 0.1%
9481
< 0.1%
9321
< 0.1%
7251
< 0.1%
6311
< 0.1%
6111
< 0.1%
5921
< 0.1%
5901
< 0.1%
5891
< 0.1%

food
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)< 0.1%
Missing484072
Missing (%)44.7%
Infinite0
Infinite (%)0.0%
Mean4.104178868
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:24.677162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median4
Q34.5
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5642075197
Coefficient of variation (CV)0.1374714743
Kurtosis1.385907272
Mean4.104178868
Median Absolute Deviation (MAD)0.5
Skewness-0.9138337666
Sum2459737
Variance0.3183301253
MonotonicityNot monotonic
2021-12-08T01:05:25.126817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4.5221346
20.4%
4197437
18.2%
3.587173
 
8.0%
549108
 
4.5%
330317
 
2.8%
2.59843
 
0.9%
23251
 
0.3%
1.5752
 
0.1%
198
 
< 0.1%
(Missing)484072
44.7%
ValueCountFrequency (%)
198
 
< 0.1%
1.5752
 
0.1%
23251
 
0.3%
2.59843
 
0.9%
330317
 
2.8%
3.587173
 
8.0%
4197437
18.2%
4.5221346
20.4%
549108
 
4.5%
ValueCountFrequency (%)
549108
 
4.5%
4.5221346
20.4%
4197437
18.2%
3.587173
 
8.0%
330317
 
2.8%
2.59843
 
0.9%
23251
 
0.3%
1.5752
 
0.1%
198
 
< 0.1%

service
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)< 0.1%
Missing479110
Missing (%)44.2%
Infinite0
Infinite (%)0.0%
Mean4.067245365
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:25.659071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median4
Q34.5
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5812667001
Coefficient of variation (CV)0.1429140973
Kurtosis1.198465749
Mean4.067245365
Median Absolute Deviation (MAD)0.5
Skewness-0.8357308358
Sum2457783.5
Variance0.3378709766
MonotonicityNot monotonic
2021-12-08T01:05:26.198189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4202956
18.7%
4.5202637
18.7%
3.598240
 
9.1%
549937
 
4.6%
334711
 
3.2%
2.510904
 
1.0%
23771
 
0.3%
1.51004
 
0.1%
1127
 
< 0.1%
(Missing)479110
44.2%
ValueCountFrequency (%)
1127
 
< 0.1%
1.51004
 
0.1%
23771
 
0.3%
2.510904
 
1.0%
334711
 
3.2%
3.598240
9.1%
4202956
18.7%
4.5202637
18.7%
549937
 
4.6%
ValueCountFrequency (%)
549937
 
4.6%
4.5202637
18.7%
4202956
18.7%
3.598240
9.1%
334711
 
3.2%
2.510904
 
1.0%
23771
 
0.3%
1.51004
 
0.1%
1127
 
< 0.1%

value
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)< 0.1%
Missing480705
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean3.982896737
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:26.764506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13.5
median4
Q34.5
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5776048366
Coefficient of variation (CV)0.1450212935
Kurtosis1.050370969
Mean3.982896737
Median Absolute Deviation (MAD)0.5
Skewness-0.7419397672
Sum2400460
Variance0.3336273473
MonotonicityNot monotonic
2021-12-08T01:05:27.112132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4217858
20.1%
4.5172698
 
15.9%
3.5118325
 
10.9%
341754
 
3.9%
534268
 
3.2%
2.512564
 
1.2%
23995
 
0.4%
1.51035
 
0.1%
1195
 
< 0.1%
(Missing)480705
44.4%
ValueCountFrequency (%)
1195
 
< 0.1%
1.51035
 
0.1%
23995
 
0.4%
2.512564
 
1.2%
341754
 
3.9%
3.5118325
10.9%
4217858
20.1%
4.5172698
15.9%
534268
 
3.2%
ValueCountFrequency (%)
534268
 
3.2%
4.5172698
15.9%
4217858
20.1%
3.5118325
10.9%
341754
 
3.9%
2.512564
 
1.2%
23995
 
0.4%
1.51035
 
0.1%
1195
 
< 0.1%

atmosphere
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)< 0.1%
Missing821612
Missing (%)75.8%
Infinite0
Infinite (%)0.0%
Mean3.93368222
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.3 MiB
2021-12-08T01:05:27.428265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13.5
median4
Q34.5
95-th percentile4.5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5552619671
Coefficient of variation (CV)0.1411557762
Kurtosis0.9718204601
Mean3.93368222
Median Absolute Deviation (MAD)0.5
Skewness-0.7228480256
Sum1029779
Variance0.3083158521
MonotonicityNot monotonic
2021-12-08T01:05:27.854861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
499726
 
9.2%
4.569508
 
6.4%
3.556092
 
5.2%
320329
 
1.9%
58686
 
0.8%
2.55439
 
0.5%
21554
 
0.1%
1.5387
 
< 0.1%
164
 
< 0.1%
(Missing)821612
75.8%
ValueCountFrequency (%)
164
 
< 0.1%
1.5387
 
< 0.1%
21554
 
0.1%
2.55439
 
0.5%
320329
 
1.9%
3.556092
5.2%
499726
9.2%
4.569508
6.4%
58686
 
0.8%
ValueCountFrequency (%)
58686
 
0.8%
4.569508
6.4%
499726
9.2%
3.556092
5.2%
320329
 
1.9%
2.55439
 
0.5%
21554
 
0.1%
1.5387
 
< 0.1%
164
 
< 0.1%

keywords
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct99001
Distinct (%)99.8%
Missing984199
Missing (%)90.8%
Memory size40.7 MiB
steak, onion loaf, lettuce wedge, chateaubriand, t bone
 
7
curry, poppadoms, rice, lamb, best indian
 
6
curry, poppadoms, rice, lamb, prawns
 
6
curry, rice, naan, lamb, prawns
 
5
curry, poppadoms, chicken, indian food, best indian
 
5
Other values (98996)
99169 

Length

Max length125
Median length54
Mean length55.35753745
Min length25

Characters and Unicode

Total characters5491357
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98859 ?
Unique (%)99.7%

Sample

1st rowpizza, tartiflette, fondue, service was excellent, chef
2nd rowtuna, fish, tapas, vineyard, region
3rd rowvisited cafe, food was fantastic, both occasions, mascarpone, courses
4th rowpates, apple tart, main course, wine list, set menu
5th rowlunch, large trees, fantastic value, courses, euros

Common Values

ValueCountFrequency (%)
steak, onion loaf, lettuce wedge, chateaubriand, t bone7
 
< 0.1%
curry, poppadoms, rice, lamb, best indian6
 
< 0.1%
curry, poppadoms, rice, lamb, prawns6
 
< 0.1%
curry, rice, naan, lamb, prawns5
 
< 0.1%
curry, poppadoms, chicken, indian food, best indian5
 
< 0.1%
curry, poppadoms, rice, lamb, bread5
 
< 0.1%
curry, poppadoms, rice, onion bhaji, lamb5
 
< 0.1%
curry, poppadoms, chicken, onion bhaji, best indian5
 
< 0.1%
curry, poppadoms, onion bhaji, rice, lamb4
 
< 0.1%
steak, onion loaf, lettuce wedge, chateaubriand, calamari4
 
< 0.1%
Other values (98991)99146
 
9.2%
(Missing)984199
90.8%

Length

2021-12-08T01:05:28.429860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
food18167
 
2.3%
steak13671
 
1.7%
salad13237
 
1.7%
and11514
 
1.4%
fish11263
 
1.4%
chicken10558
 
1.3%
great10258
 
1.3%
bread9867
 
1.2%
burger9278
 
1.2%
pizza9135
 
1.1%
Other values (14991)679867
85.3%

Most occurring characters

ValueCountFrequency (%)
697617
12.7%
e488399
 
8.9%
a469944
 
8.6%
,396792
 
7.2%
s350816
 
6.4%
i294897
 
5.4%
r293001
 
5.3%
t270768
 
4.9%
o266317
 
4.8%
n239836
 
4.4%
Other values (32)1722970
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4396492
80.1%
Space Separator697617
 
12.7%
Other Punctuation397202
 
7.2%
Decimal Number46
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e488399
 
11.1%
a469944
 
10.7%
s350816
 
8.0%
i294897
 
6.7%
r293001
 
6.7%
t270768
 
6.2%
o266317
 
6.1%
n239836
 
5.5%
c217262
 
4.9%
l198060
 
4.5%
Other values (19)1307192
29.7%
Decimal Number
ValueCountFrequency (%)
28
17.4%
56
13.0%
16
13.0%
95
10.9%
35
10.9%
84
8.7%
04
8.7%
63
 
6.5%
73
 
6.5%
42
 
4.3%
Other Punctuation
ValueCountFrequency (%)
,396792
99.9%
'410
 
0.1%
Space Separator
ValueCountFrequency (%)
697617
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4396492
80.1%
Common1094865
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e488399
 
11.1%
a469944
 
10.7%
s350816
 
8.0%
i294897
 
6.7%
r293001
 
6.7%
t270768
 
6.2%
o266317
 
6.1%
n239836
 
5.5%
c217262
 
4.9%
l198060
 
4.5%
Other values (19)1307192
29.7%
Common
ValueCountFrequency (%)
697617
63.7%
,396792
36.2%
'410
 
< 0.1%
28
 
< 0.1%
56
 
< 0.1%
16
 
< 0.1%
95
 
< 0.1%
35
 
< 0.1%
84
 
< 0.1%
04
 
< 0.1%
Other values (3)8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5490861
> 99.9%
None496
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
697617
12.7%
e488399
 
8.9%
a469944
 
8.6%
,396792
 
7.2%
s350816
 
6.4%
i294897
 
5.4%
r293001
 
5.3%
t270768
 
4.9%
o266317
 
4.9%
n239836
 
4.4%
Other values (29)1722474
31.4%
None
ValueCountFrequency (%)
é271
54.6%
û215
43.3%
â10
 
2.0%

Interactions

2021-12-08T01:03:42.409192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:19.962165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:29.565821image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:37.966065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:53.492700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:02.698114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:10.294851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:21.251559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:31.476796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:41.143996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:48.504702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:56.764169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:04.914674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:13.372093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:21.014036image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:29.192650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:35.709478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:42.678312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:20.590320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:30.148994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:38.391909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:54.045362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:03.077202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:10.839833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:21.935187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:32.317727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:41.571430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:48.958851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:57.273913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:05.380705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:13.886435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:21.397797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:29.644800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:36.064771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:42.983321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:21.001618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:30.542329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:39.429094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:54.707905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:03.550780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:11.242466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:22.691809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:32.969624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:41.905612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:49.287409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:57.629514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:05.781258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:14.408408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:21.755829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-12-08T01:03:11.873715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:19.904759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:27.473716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:34.442874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:41.350957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:46.772979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:28.503562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:36.963334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:52.556728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:01.806374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:09.506677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:20.367303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:30.333547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:40.276262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:47.695715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:55.709951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:03.956605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:12.427357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:20.273014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:28.043335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:34.841697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:41.736384image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:47.089110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:28.933446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:37.555450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:01:52.966331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:02.193278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:09.793915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:20.719666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:30.770051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:40.632894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:48.018076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:02:56.088068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:04.406306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:12.798391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:20.586565image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:28.485322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:35.143141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-12-08T01:03:42.038769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-12-08T01:05:29.074516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-08T01:05:29.739404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-08T01:05:30.525078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-08T01:05:31.188650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-12-08T01:05:31.726834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-08T01:04:00.917324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-08T01:04:10.153535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-08T01:04:40.274596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-08T01:04:54.321807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

restaurant_linkrestaurant_nameoriginal_locationcountryregionprovincecityaddresslatitudelongitudeclaimedawardspopularity_detailedpopularity_generictop_tagsprice_levelprice_rangemealscuisinesspecial_dietsfeaturesvegetarian_friendlyvegan_optionsgluten_freeoriginal_open_hoursopen_days_per_weekopen_hours_per_weekworking_shifts_per_weekavg_ratingtotal_reviews_countdefault_languagereviews_count_in_default_languageexcellentvery_goodaveragepoorterriblefoodservicevalueatmospherekeywords
0g10001637-d10002227Le 147["Europe", "France", "Nouvelle-Aquitaine", "Haute-Vienne", "Saint-Jouvent"]FranceNouvelle-AquitaineHaute-VienneSaint-Jouvent10 Maison Neuve, 87510 Saint-Jouvent France45.9616741.169131ClaimedNaN#1 of 2 Restaurants in Saint-Jouvent#1 of 2 places to eat in Saint-JouventCheap Eats, FrenchNaNLunch, DinnerFrenchNaNReservations, Seating, Wheelchair Accessible, Serves Alcohol, Accepts Credit Cards, Table ServiceNNNNaNNaNNaNNaN4.036.0English2.02.00.00.00.00.04.04.54.0NaNNaN
1g10001637-d14975787Le Saint Jouvent["Europe", "France", "Nouvelle-Aquitaine", "Haute-Vienne", "Saint-Jouvent"]FranceNouvelle-AquitaineHaute-VienneSaint-Jouvent16 Place de l Eglise, 87510 Saint-Jouvent France45.9570401.205480UnclaimedNaN#2 of 2 Restaurants in Saint-Jouvent#2 of 2 places to eat in Saint-JouventCheap EatsNaNNaNNaNNaNNaNNNNNaNNaNNaNNaN4.05.0All languages5.02.02.01.00.00.0NaNNaNNaNNaNNaN
2g10002858-d4586832Au Bout du Pont["Europe", "France", "Centre-Val de Loire", "Berry", "Indre", "Rivarennes"]FranceCentre-Val de LoireBerryRivarennes2 rue des Dames, 36800 Rivarennes France46.6358951.386133ClaimedNaN#1 of 1 Restaurant in Rivarennes#1 of 1 places to eat in RivarennesCheap Eats, French, EuropeanNaNDinner, Lunch, DrinksFrench, EuropeanNaNReservations, Seating, Table Service, Wheelchair AccessibleNNNNaNNaNNaNNaN5.013.0English4.03.01.00.00.00.0NaNNaNNaNNaNNaN
3g10002986-d3510044Le Relais de Naiade["Europe", "France", "Nouvelle-Aquitaine", "Correze", "Lacelle"]FranceNouvelle-AquitaineCorrezeLacelle9 avenue Porte de la Correze 19170, 19170 Lacelle France45.6426101.824460ClaimedNaN#1 of 1 Restaurant in Lacelle#1 of 1 places to eat in LacelleCheap Eats, FrenchNaNLunch, DinnerFrenchNaNReservations, Seating, Serves Alcohol, Table Service, Wheelchair AccessibleNNNNaNNaNNaNNaN4.034.0English1.01.00.00.00.00.04.54.54.5NaNNaN
4g10022428-d9767191Relais Du MontSeigne["Europe", "France", "Occitanie", "Aveyron", "Saint-Laurent-de-Levezou"]FranceOccitanieAveyronSaint-Laurent-de-Levezouroute du Montseigne, 12620 Saint-Laurent-de-Levezou France44.2088602.960470UnclaimedNaN#1 of 1 Restaurant in Saint-Laurent-de-Levezou#1 of 1 places to eat in Saint-Laurent-de-LevezouMid-range, French€€-€€€NaNLunch, DinnerFrenchNaNReservations, Seating, Wheelchair Accessible, Table ServiceNNNNaNNaNNaNNaN4.511.0All languages11.04.07.00.00.00.04.54.54.5NaNNaN
5g10029260-d6605477L'Auberge Du Vieux Crozet["Europe", "France", "Auvergne-Rhone-Alpes", "Loire", "Roanne", "Le Crozet"]FranceAuvergne-Rhone-AlpesLoireLe Crozet59 place du Puits ancienne adresse le Bourg renommée 59 place du Puits, 42310 Le Crozet, Roanne France46.1698233.855819ClaimedTravellers' Choice, Certificate of Excellence 2020#1 of 1 Restaurant in Le Crozet#1 of 1 places to eat in Le CrozetMid-range, French€€-€€€€14-€29Lunch, Dinner, DrinksFrenchNaNNaNNNN{"Mon": ["09:00-14:30"], "Tue": ["09:00-14:30", "19:00-21:30"], "Wed": ["09:00-14:30", "19:00-21:30"], "Thu": ["09:00-14:30", "19:00-21:30"], "Fri": ["09:00-14:30", "19:00-22:00"], "Sat": ["09:00-14:30", "19:00-22:00"], "Sun": ["09:00-16:00"]}7.053.512.04.564.0All languages64.044.015.02.02.01.04.54.54.5NaNNaN
6g10029907-d17781655Cafe Restaurant NouLou["Europe", "France", "Occitanie", "Aude", "Saint-Denis"]FranceOccitanieAudeSaint-DenisPlace de l'Église, 30500 Saint-Denis France44.2330784.251449ClaimedNaN#2 of 2 Restaurants in Saint-Denis#2 of 2 places to eat in Saint-DenisMid-range, French, European€€-€€€€8-€17Lunch, DinnerFrench, EuropeanNaNNaNNNN{"Mon": [], "Tue": [], "Wed": ["12:00-14:30", "18:30-22:00"], "Thu": ["12:00-14:30", "18:30-22:00"], "Fri": ["12:00-14:30", "18:30-22:00"], "Sat": ["12:00-14:30", "18:30-22:00"], "Sun": ["12:00-14:30", "18:30-22:00"]}5.030.010.04.524.0English4.04.00.00.00.00.04.54.54.5NaNNaN
7g10029907-d8079764L'entre 2["Europe", "France", "Occitanie", "Aude", "Saint-Denis"]FranceOccitanieAudeSaint-Denis4 route de Saissac, 11310 Saint-Denis France43.3600232.219851ClaimedTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017#1 of 2 Restaurants in Saint-Denis#1 of 2 places to eat in Saint-DenisMid-range, French, European, Vegetarian Friendly€€-€€€€10-€35NaNFrench, EuropeanVegetarian FriendlyNaNYNN{"Mon": [], "Tue": ["10:00-14:00"], "Wed": ["10:00-14:00"], "Thu": ["10:00-14:00"], "Fri": ["10:00-14:00"], "Sat": ["10:00-14:00"], "Sun": ["10:00-14:00"]}6.024.06.04.5133.0English13.09.03.01.00.00.04.54.54.5NaNNaN
8g10036850-d8414223Noste Courtiu["Europe", "France", "Occitanie", "Ariege", "Orgibet"]FranceOccitanieAriegeOrgibetroute des Pyrenees, 09800 Orgibet France42.9340000.936559ClaimedNaN#1 of 1 Restaurant in Orgibet#1 of 1 places to eat in OrgibetMid-range, French, Cafe, Deli€€-€€€€12-€26Lunch, Dinner, DrinksFrench, Cafe, Deli, Contemporary, GastropubNaNNaNNNN{"Mon": [], "Tue": [], "Wed": ["12:00-14:00"], "Thu": ["12:00-14:00"], "Fri": ["12:00-14:00", "19:00-21:00"], "Sat": ["19:00-21:00"], "Sun": ["12:00-14:00", "19:00-21:00"]}5.014.07.05.039.0English2.02.00.00.00.00.04.54.54.5NaNNaN
9g10054961-d3387712Chez Claudine["Europe", "France", "Grand Est", "Vosges", "They-sous-Montfort"]FranceGrand EstVosgesThey-sous-Montfort136 rue de la Petite They, 88800 They-sous-Montfort France48.2314955.973734ClaimedTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016, Certificate of Excellence 2015#1 of 1 Restaurant in They-sous-Montfort#1 of 1 places to eat in They-sous-MontfortMid-range, French, European, Wine Bar€€-€€€€12-€30After-hours, Drinks, Lunch, Dinner, BrunchFrench, European, Wine BarNaNNaNNNN{"Mon": [], "Tue": ["09:00-16:00"], "Wed": ["09:00-16:00"], "Thu": ["09:00-21:00"], "Fri": ["09:00-21:00"], "Sat": ["16:45-23:45"], "Sun": ["09:00-17:00"]}6.053.06.04.5244.0English1.01.00.00.00.00.04.54.54.54.5NaN

Last rows

restaurant_linkrestaurant_nameoriginal_locationcountryregionprovincecityaddresslatitudelongitudeclaimedawardspopularity_detailedpopularity_generictop_tagsprice_levelprice_rangemealscuisinesspecial_dietsfeaturesvegetarian_friendlyvegan_optionsgluten_freeoriginal_open_hoursopen_days_per_weekopen_hours_per_weekworking_shifts_per_weekavg_ratingtotal_reviews_countdefault_languagereviews_count_in_default_languageexcellentvery_goodaveragepoorterriblefoodservicevalueatmospherekeywords
1083387g946544-d4753627Pensiunea Restaurant Rosenau["Europe", "Romania", "Transylvania", "Central Romania", "Brasov County", "Rasnov"]RomaniaTransylvaniaBrasov CountyRasnovStr. Florilor 27, Rasnov 505400 Romania45.59276025.462336UnclaimedNaN#13 of 14 Restaurants in Rasnov#13 of 15 places to eat in RasnovMid-range, European, Eastern European, Romanian€€-€€€NaNNaNEuropean, Romanian, Eastern EuropeanNaNSeating, Serves Alcohol, Reservations, Table ServiceNNN{"Mon": ["10:00-00:00"], "Tue": ["10:00-00:00"], "Wed": ["10:00-00:00"], "Thu": ["10:00-00:00"], "Fri": ["10:00-00:00"], "Sat": ["10:00-00:00"], "Sun": ["10:00-00:00"]}7.098.07.03.061.0English43.07.013.08.06.09.03.53.03.54.0NaN
1083388g946544-d7128597La Promenada["Europe", "Romania", "Transylvania", "Central Romania", "Brasov County", "Rasnov"]RomaniaTransylvaniaBrasov CountyRasnovStr. Teilor nr. 88, Rasnov 505400 Romania45.59791025.472660ClaimedTravellers' Choice, Certificate of Excellence 2020, Certificate of Excellence 2019, Certificate of Excellence 2018, Certificate of Excellence 2017, Certificate of Excellence 2016#1 of 14 Restaurants in Rasnov#1 of 15 places to eat in RasnovMid-range, Romanian, Vegetarian Friendly, Vegan Options€€-€€€NaNBreakfast, Lunch, Dinner, BrunchRomanianVegetarian Friendly, Vegan Options, Gluten Free OptionsNaNYYY{"Mon": ["13:00-23:00"], "Tue": ["11:00-23:00"], "Wed": ["11:00-23:00"], "Thu": ["11:00-23:00"], "Fri": ["10:00-23:45"], "Sat": ["10:00-23:45"], "Sun": ["10:00-23:00"]}7.086.57.04.0355.0English266.0159.048.025.011.023.04.54.04.5NaNgoulash, soup, pork, outdoor playground, nice meal
1083389g946544-d8490226Papazaur["Europe", "Romania", "Transylvania", "Central Romania", "Brasov County", "Rasnov"]RomaniaTransylvaniaBrasov CountyRasnovStrada Cet ii, Rasnov 505400 Romania45.59161825.474144UnclaimedNaN#12 of 14 Restaurants in Rasnov#12 of 15 places to eat in RasnovMid-range, Fast food, European€€-€€€NaNLunch, BrunchFast food, EuropeanNaNSeatingNNNNaNNaNNaNNaN3.521.0English19.03.07.04.03.02.04.04.54.0NaNNaN
1083390g946544-d8749966Intim["Europe", "Romania", "Transylvania", "Central Romania", "Brasov County", "Rasnov"]RomaniaTransylvaniaBrasov CountyRasnovStrada Ion Creanga 1, Rasnov 505400 Romania45.59078625.463972UnclaimedNaN#8 of 14 Restaurants in Rasnov#8 of 15 places to eat in RasnovMid-range, Eastern European, Romanian€€-€€€NaNLunch, Dinner, BrunchEastern European, RomanianNaNReservations, Wheelchair Accessible, Outdoor Seating, Seating, Table ServiceNNNNaNNaNNaNNaN4.022.0English16.05.06.04.00.01.0NaNNaNNaNNaNNaN
1083391g9610184-d19807817Casa Amicii["Europe", "Romania", "Transylvania", "Western Romania", "Hunedoara County", "Uricani"]RomaniaTransylvaniaHunedoara CountyUricaniAleea Teilor 34, Uricani 336100 Romania45.33302023.124910UnclaimedNaN#1 of 1 Restaurant in Uricani#1 of 1 places to eat in UricaniEuropean, RomanianNaNNaNNaNEuropean, RomanianNaNNaNNNNNaNNaNNaNNaN5.01.0All languages1.01.00.00.00.00.0NaNNaNNaNNaNNaN
1083392g9710275-d10770782Complex Popas Pacurari["Europe", "Romania", "Northeast Romania", "Iasi County", "Valea Lupului"]RomaniaNortheast RomaniaIasi CountyNaNSoseaua Pacurari, Valea Lupului 707410 Romania47.17295027.519110UnclaimedNaN#1 of 1 Restaurant in Valea Lupului#1 of 1 places to eat in Valea LupuluiNaNNaNNaNLunch, DinnerNaNNaNNaNNNN{"Mon": ["10:00-22:00"], "Tue": ["10:00-22:00"], "Wed": ["10:00-22:00"], "Thu": ["10:00-22:00"], "Fri": ["10:00-22:00"], "Sat": ["10:00-22:00"], "Sun": ["10:00-22:00"]}7.084.07.02.52.0English1.00.00.00.00.01.0NaNNaNNaNNaNNaN
1083393g9716321-d15026574Casa Pastravarului DORIPESCO["Europe", "Romania", "Transylvania", "Central Romania", "Brasov County", "Apata"]RomaniaTransylvaniaBrasov CountyApataDN 13 Judetul Kilometrul 33 Maierus, Apata 507005 Romania45.90442325.470509ClaimedNaN#1 of 1 Restaurant in Apata#1 of 1 places to eat in ApataMid-range, Eastern European€€-€€€NaNBreakfast, Lunch, Dinner, Brunch, DrinksEastern EuropeanNaNNaNNNN{"Mon": ["08:00-22:00"], "Tue": ["08:00-22:00"], "Wed": ["08:00-22:00"], "Thu": ["08:00-22:00"], "Fri": ["08:00-22:00"], "Sat": ["08:00-22:00"], "Sun": ["08:00-22:00"]}7.098.07.02.06.0English5.00.01.01.01.02.0NaNNaNNaNNaNNaN
1083394g9722813-d15891057Hanul Tentea["Europe", "Romania", "Transylvania", "Northwest Romania", "Maramures County", "Sacel"]RomaniaTransylvaniaMaramures CountySacelDN17C, Sacel Romania47.63192024.450910UnclaimedNaN#1 of 1 Restaurant in Sacel#1 of 1 places to eat in SacelNaNNaNNaNNaNNaNNaNNaNNNNNaNNaNNaNNaN3.02.0English2.01.00.00.00.01.0NaNNaNNaNNaNNaN
1083395g9726871-d21391722Casa Paduraru["Europe", "Romania", "Southern Romania", "Arges County", "Maracineni"]RomaniaSouthern RomaniaArges CountyNaNSat. Argeselu Numarul 432, Maracineni 117450 Romania44.91895024.867634ClaimedNaNNaNNaNCheap Eats, French, American, Bar€2-€8Breakfast, Lunch, Dinner, Brunch, DrinksFrench, American, Bar, International, European, Pub, RomanianNaNNaNNNN{"Mon": ["10:00-21:00"], "Tue": ["10:00-21:00"], "Wed": ["10:00-21:00"], "Thu": ["10:00-21:00"], "Fri": ["10:00-21:00"], "Sat": [], "Sun": []}5.055.05.0NaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1083396g9867250-d14979687Pastravaria Alina Sarbi["Europe", "Romania", "Transylvania", "Northwest Romania", "Maramures County", "Budesti"]RomaniaTransylvaniaMaramures CountyBudestiStr. Principala Nr 166A, Budesti 437071 Romania47.75222023.938343UnclaimedNaN#1 of 1 Restaurant in Budesti#1 of 1 places to eat in BudestiDinerNaNNaNNaNDinerNaNNaNNNNNaNNaNNaNNaN1.53.0English2.00.00.01.00.01.0NaNNaNNaNNaNNaN